id	sid	tid	token	lemma	pos
fcis-7234	1	1	frontiers	frontier	NOUN
fcis-7234	1	2	in	in	ADP
fcis-7234	1	3	computing	computing	NOUN
fcis-7234	1	4	and	and	CCONJ
fcis-7234	1	5	intelligent	intelligent	ADJ
fcis-7234	1	6	systems	system	NOUN
fcis-7234	1	7	issn	issn	VERB
fcis-7234	1	8	:	:	PUNCT
fcis-7234	1	9	2832	2832	NUM
fcis-7234	1	10	-	-	SYM
fcis-7234	1	11	6024	6024	NUM
fcis-7234	1	12	|	|	NOUN
fcis-7234	1	13	vol	vol	NOUN
fcis-7234	1	14	.	.	PROPN
fcis-7234	2	1	3	3	NUM
fcis-7234	2	2	,	,	PUNCT
fcis-7234	2	3	no	no	INTJ
fcis-7234	2	4	.	.	NOUN
fcis-7234	2	5	2	2	NUM
fcis-7234	2	6	,	,	PUNCT
fcis-7234	2	7	2023	2023	NUM
fcis-7234	2	8	58	58	NUM
fcis-7234	2	9	research	research	NOUN
fcis-7234	2	10	on	on	ADP
fcis-7234	2	11	no	no	DET
fcis-7234	2	12	-	-	PUNCT
fcis-7234	2	13	reference	reference	NOUN
fcis-7234	2	14	image	image	NOUN
fcis-7234	2	15	quality	quality	NOUN
fcis-7234	2	16	assessment	assessment	NOUN
fcis-7234	2	17	algorithm	algorithm	NOUN
fcis-7234	2	18	based	base	VERB
fcis-7234	2	19	on	on	ADP
fcis-7234	2	20	generative	generative	ADJ
fcis-7234	2	21	adversarial	adversarial	ADJ
fcis-7234	2	22	networks	network	NOUN
fcis-7234	2	23	wenqing	wenqe	VERB
fcis-7234	2	24	zhao	zhao	PROPN
fcis-7234	2	25	,	,	PUNCT
fcis-7234	2	26	haoyang	haoyang	PROPN
fcis-7234	2	27	chen	chen	PROPN
fcis-7234	2	28	school	school	PROPN
fcis-7234	2	29	of	of	ADP
fcis-7234	2	30	control	control	NOUN
fcis-7234	2	31	and	and	CCONJ
fcis-7234	2	32	computer	computer	NOUN
fcis-7234	2	33	engineering	engineering	NOUN
fcis-7234	2	34	,	,	PUNCT
fcis-7234	2	35	north	north	PROPN
fcis-7234	2	36	china	china	PROPN
fcis-7234	2	37	electric	electric	PROPN
fcis-7234	2	38	power	power	PROPN
fcis-7234	2	39	university	university	PROPN
fcis-7234	2	40	,	,	PUNCT
fcis-7234	2	41	baoding	baoding	PROPN
fcis-7234	2	42	071003	071003	NUM
fcis-7234	2	43	,	,	PUNCT
fcis-7234	2	44	china	china	PROPN
fcis-7234	2	45	abstract	abstract	NOUN
fcis-7234	2	46	:	:	PUNCT
fcis-7234	2	47	currently	currently	ADV
fcis-7234	2	48	,	,	PUNCT
fcis-7234	2	49	with	with	ADP
fcis-7234	2	50	the	the	DET
fcis-7234	2	51	massive	massive	ADJ
fcis-7234	2	52	generation	generation	NOUN
fcis-7234	2	53	and	and	CCONJ
fcis-7234	2	54	transmission	transmission	NOUN
fcis-7234	2	55	of	of	ADP
fcis-7234	2	56	digital	digital	ADJ
fcis-7234	2	57	images	image	NOUN
fcis-7234	2	58	,	,	PUNCT
fcis-7234	2	59	especially	especially	ADV
fcis-7234	2	60	the	the	DET
fcis-7234	2	61	rapid	rapid	ADJ
fcis-7234	2	62	development	development	NOUN
fcis-7234	2	63	of	of	ADP
fcis-7234	2	64	the	the	DET
fcis-7234	2	65	internet	internet	NOUN
fcis-7234	2	66	and	and	CCONJ
fcis-7234	2	67	mobile	mobile	ADJ
fcis-7234	2	68	internet	internet	NOUN
fcis-7234	2	69	industries	industry	NOUN
fcis-7234	2	70	where	where	SCONJ
fcis-7234	2	71	images	image	NOUN
fcis-7234	2	72	are	be	AUX
fcis-7234	2	73	widely	widely	ADV
fcis-7234	2	74	used	use	VERB
fcis-7234	2	75	,	,	PUNCT
fcis-7234	2	76	the	the	DET
fcis-7234	2	77	study	study	NOUN
fcis-7234	2	78	of	of	ADP
fcis-7234	2	79	reference	reference	NOUN
fcis-7234	2	80	-	-	PUNCT
fcis-7234	2	81	free	free	ADJ
fcis-7234	2	82	image	image	NOUN
fcis-7234	2	83	quality	quality	NOUN
fcis-7234	2	84	assessment	assessment	NOUN
fcis-7234	2	85	has	have	AUX
fcis-7234	2	86	attracted	attract	VERB
fcis-7234	2	87	much	much	ADJ
fcis-7234	2	88	attention	attention	NOUN
fcis-7234	2	89	in	in	ADP
fcis-7234	2	90	academia	academia	NOUN
fcis-7234	2	91	and	and	CCONJ
fcis-7234	2	92	practical	practical	ADJ
fcis-7234	2	93	applications	application	NOUN
fcis-7234	2	94	,	,	PUNCT
fcis-7234	2	95	and	and	CCONJ
fcis-7234	2	96	is	be	AUX
fcis-7234	2	97	a	a	DET
fcis-7234	2	98	popular	popular	ADJ
fcis-7234	2	99	research	research	NOUN
fcis-7234	2	100	direction	direction	NOUN
fcis-7234	2	101	in	in	ADP
fcis-7234	2	102	the	the	DET
fcis-7234	2	103	field	field	NOUN
fcis-7234	2	104	of	of	ADP
fcis-7234	2	105	computer	computer	NOUN
fcis-7234	2	106	vision	vision	NOUN
fcis-7234	2	107	.	.	PUNCT
fcis-7234	3	1	in	in	ADP
fcis-7234	3	2	response	response	NOUN
fcis-7234	3	3	to	to	ADP
fcis-7234	3	4	the	the	DET
fcis-7234	3	5	low	low	ADJ
fcis-7234	3	6	performance	performance	NOUN
fcis-7234	3	7	of	of	ADP
fcis-7234	3	8	existing	exist	VERB
fcis-7234	3	9	reference	reference	NOUN
fcis-7234	3	10	-	-	PUNCT
fcis-7234	3	11	free	free	ADJ
fcis-7234	3	12	image	image	NOUN
fcis-7234	3	13	quality	quality	NOUN
fcis-7234	3	14	assessment	assessment	NOUN
fcis-7234	3	15	algorithms	algorithm	NOUN
fcis-7234	3	16	in	in	ADP
fcis-7234	3	17	the	the	DET
fcis-7234	3	18	face	face	NOUN
fcis-7234	3	19	of	of	ADP
fcis-7234	3	20	real	real	ADJ
fcis-7234	3	21	distorted	distort	VERB
fcis-7234	3	22	images	image	NOUN
fcis-7234	3	23	,	,	PUNCT
fcis-7234	3	24	a	a	DET
fcis-7234	3	25	reference	reference	NOUN
fcis-7234	3	26	-	-	PUNCT
fcis-7234	3	27	free	free	ADJ
fcis-7234	3	28	image	image	NOUN
fcis-7234	3	29	quality	quality	NOUN
fcis-7234	3	30	assessment	assessment	NOUN
fcis-7234	3	31	algorithm	algorithm	NOUN
fcis-7234	3	32	based	base	VERB
fcis-7234	3	33	on	on	ADP
fcis-7234	3	34	generative	generative	ADJ
fcis-7234	3	35	adversarial	adversarial	ADJ
fcis-7234	3	36	networks	network	NOUN
fcis-7234	3	37	is	be	AUX
fcis-7234	3	38	proposed	propose	VERB
fcis-7234	3	39	.	.	PUNCT
fcis-7234	4	1	firstly	firstly	ADV
fcis-7234	4	2	,	,	PUNCT
fcis-7234	4	3	the	the	DET
fcis-7234	4	4	generator	generator	NOUN
fcis-7234	4	5	structure	structure	NOUN
fcis-7234	4	6	is	be	AUX
fcis-7234	4	7	changed	change	VERB
fcis-7234	4	8	,	,	PUNCT
fcis-7234	4	9	the	the	DET
fcis-7234	4	10	u	u	ADJ
fcis-7234	4	11	-	-	ADJ
fcis-7234	4	12	net	net	ADJ
fcis-7234	4	13	structure	structure	NOUN
fcis-7234	4	14	is	be	AUX
fcis-7234	4	15	improved	improve	VERB
fcis-7234	4	16	,	,	PUNCT
fcis-7234	4	17	and	and	CCONJ
fcis-7234	4	18	the	the	DET
fcis-7234	4	19	channel	channel	NOUN
fcis-7234	4	20	attention	attention	NOUN
fcis-7234	4	21	mechanism	mechanism	NOUN
fcis-7234	4	22	senet	senet	NOUN
fcis-7234	4	23	structure	structure	NOUN
fcis-7234	4	24	is	be	AUX
fcis-7234	4	25	introduced	introduce	VERB
fcis-7234	4	26	to	to	PART
fcis-7234	4	27	update	update	VERB
fcis-7234	4	28	the	the	DET
fcis-7234	4	29	feature	feature	NOUN
fcis-7234	4	30	map	map	NOUN
fcis-7234	4	31	after	after	ADP
fcis-7234	4	32	down	down	ADP
fcis-7234	4	33	sampling	sample	VERB
fcis-7234	4	34	.	.	PUNCT
fcis-7234	5	1	secondly	secondly	ADV
fcis-7234	5	2	,	,	PUNCT
fcis-7234	5	3	a	a	DET
fcis-7234	5	4	feature	feature	NOUN
fcis-7234	5	5	similarity	similarity	NOUN
fcis-7234	5	6	measurement	measurement	NOUN
fcis-7234	5	7	system	system	NOUN
fcis-7234	5	8	is	be	AUX
fcis-7234	5	9	incorporated	incorporate	VERB
fcis-7234	5	10	and	and	CCONJ
fcis-7234	5	11	a	a	DET
fcis-7234	5	12	dual	dual	ADJ
fcis-7234	5	13	discriminator	discriminator	NOUN
fcis-7234	5	14	structure	structure	NOUN
fcis-7234	5	15	is	be	AUX
fcis-7234	5	16	used	use	VERB
fcis-7234	5	17	to	to	PART
fcis-7234	5	18	discriminate	discriminate	VERB
fcis-7234	5	19	multiple	multiple	ADJ
fcis-7234	5	20	groups	group	NOUN
fcis-7234	5	21	of	of	ADP
fcis-7234	5	22	images	image	NOUN
fcis-7234	5	23	.	.	PUNCT
fcis-7234	6	1	the	the	DET
fcis-7234	6	2	fpn	fpn	PROPN
fcis-7234	6	3	structure	structure	NOUN
fcis-7234	6	4	is	be	AUX
fcis-7234	6	5	combined	combine	VERB
fcis-7234	6	6	in	in	ADP
fcis-7234	6	7	the	the	DET
fcis-7234	6	8	feature	feature	NOUN
fcis-7234	6	9	extractor	extractor	NOUN
fcis-7234	6	10	to	to	PART
fcis-7234	6	11	produce	produce	VERB
fcis-7234	6	12	a	a	DET
fcis-7234	6	13	multi	multi	ADJ
fcis-7234	6	14	-	-	ADJ
fcis-7234	6	15	scale	scale	ADJ
fcis-7234	6	16	feature	feature	NOUN
fcis-7234	6	17	representation	representation	NOUN
fcis-7234	6	18	.	.	PUNCT
fcis-7234	7	1	experiments	experiment	NOUN
fcis-7234	7	2	are	be	AUX
fcis-7234	7	3	conducted	conduct	VERB
fcis-7234	7	4	on	on	ADP
fcis-7234	7	5	koniq-10k	koniq-10k	PROPN
fcis-7234	7	6	dataset	dataset	NOUN
fcis-7234	7	7	and	and	CCONJ
fcis-7234	7	8	livec	livec	VERB
fcis-7234	7	9	dataset	dataset	NOUN
fcis-7234	7	10	,	,	PUNCT
fcis-7234	7	11	and	and	CCONJ
fcis-7234	7	12	the	the	DET
fcis-7234	7	13	experimental	experimental	ADJ
fcis-7234	7	14	results	result	NOUN
fcis-7234	7	15	show	show	VERB
fcis-7234	7	16	that	that	SCONJ
fcis-7234	7	17	the	the	DET
fcis-7234	7	18	algorithm	algorithm	NOUN
fcis-7234	7	19	exhibits	exhibit	VERB
fcis-7234	7	20	good	good	ADJ
fcis-7234	7	21	prediction	prediction	NOUN
fcis-7234	7	22	accuracy	accuracy	NOUN
fcis-7234	7	23	as	as	ADV
fcis-7234	7	24	well	well	ADV
fcis-7234	7	25	as	as	ADP
fcis-7234	7	26	good	good	ADJ
fcis-7234	7	27	generalization	generalization	NOUN
fcis-7234	7	28	performance	performance	NOUN
fcis-7234	7	29	in	in	ADP
fcis-7234	7	30	the	the	DET
fcis-7234	7	31	face	face	NOUN
fcis-7234	7	32	of	of	ADP
fcis-7234	7	33	real	real	ADJ
fcis-7234	7	34	distorted	distort	VERB
fcis-7234	7	35	images	image	NOUN
fcis-7234	7	36	.	.	PUNCT
fcis-7234	8	1	keywords	keyword	NOUN
fcis-7234	8	2	:	:	PUNCT
fcis-7234	8	3	image	image	NOUN
fcis-7234	8	4	quality	quality	NOUN
fcis-7234	8	5	assessment	assessment	NOUN
fcis-7234	8	6	;	;	PUNCT
fcis-7234	8	7	deep	deep	ADJ
fcis-7234	8	8	learning	learning	NOUN
fcis-7234	8	9	;	;	PUNCT
fcis-7234	8	10	generative	generative	ADJ
fcis-7234	8	11	adversarial	adversarial	ADJ
fcis-7234	8	12	networks	network	NOUN
fcis-7234	8	13	(	(	PUNCT
fcis-7234	8	14	gans	gan	NOUN
fcis-7234	8	15	)	)	PUNCT
fcis-7234	8	16	;	;	PUNCT
fcis-7234	8	17	convolutional	convolutional	ADJ
fcis-7234	8	18	neural	neural	ADJ
fcis-7234	8	19	networks	network	NOUN
fcis-7234	8	20	(	(	PUNCT
fcis-7234	8	21	cnns	cnns	PROPN
fcis-7234	8	22	)	)	PUNCT
fcis-7234	8	23	;	;	PUNCT
fcis-7234	8	24	feature	feature	NOUN
fcis-7234	8	25	extraction	extraction	NOUN
fcis-7234	8	26	;	;	PUNCT
fcis-7234	8	27	multi	multi	ADJ
fcis-7234	8	28	-	-	ADJ
fcis-7234	8	29	scale	scale	ADJ
fcis-7234	8	30	feature	feature	NOUN
fcis-7234	8	31	fusion	fusion	NOUN
fcis-7234	8	32	.	.	PUNCT
fcis-7234	9	1	1	1	X
fcis-7234	9	2	.	.	X
fcis-7234	9	3	introduction	introduction	NOUN
fcis-7234	9	4	as	as	ADP
fcis-7234	9	5	an	an	DET
fcis-7234	9	6	important	important	ADJ
fcis-7234	9	7	research	research	NOUN
fcis-7234	9	8	direction	direction	NOUN
fcis-7234	9	9	in	in	ADP
fcis-7234	9	10	the	the	DET
fcis-7234	9	11	field	field	NOUN
fcis-7234	9	12	of	of	ADP
fcis-7234	9	13	computer	computer	NOUN
fcis-7234	9	14	vision	vision	NOUN
fcis-7234	9	15	,	,	PUNCT
fcis-7234	9	16	image	image	NOUN
fcis-7234	9	17	quality	quality	NOUN
fcis-7234	9	18	assessment	assessment	NOUN
fcis-7234	9	19	aims	aim	VERB
fcis-7234	9	20	to	to	PART
fcis-7234	9	21	objectively	objectively	ADV
fcis-7234	9	22	assess	assess	VERB
fcis-7234	9	23	the	the	DET
fcis-7234	9	24	quality	quality	NOUN
fcis-7234	9	25	of	of	ADP
fcis-7234	9	26	images	image	NOUN
fcis-7234	9	27	through	through	ADP
fcis-7234	9	28	computer	computer	NOUN
fcis-7234	9	29	algorithms	algorithm	NOUN
fcis-7234	9	30	.	.	PUNCT
fcis-7234	10	1	it	it	PRON
fcis-7234	10	2	is	be	AUX
fcis-7234	10	3	mainly	mainly	ADV
fcis-7234	10	4	applied	apply	VERB
fcis-7234	10	5	in	in	ADP
fcis-7234	10	6	the	the	DET
fcis-7234	10	7	fields	field	NOUN
fcis-7234	10	8	of	of	ADP
fcis-7234	10	9	image	image	NOUN
fcis-7234	10	10	processing	processing	NOUN
fcis-7234	10	11	,	,	PUNCT
fcis-7234	10	12	video	video	NOUN
fcis-7234	10	13	transmission	transmission	NOUN
fcis-7234	10	14	,	,	PUNCT
fcis-7234	10	15	and	and	CCONJ
fcis-7234	10	16	digital	digital	ADJ
fcis-7234	10	17	media	medium	NOUN
fcis-7234	10	18	.	.	PUNCT
fcis-7234	11	1	reference	reference	NOUN
fcis-7234	11	2	-	-	PUNCT
fcis-7234	11	3	free	free	ADJ
fcis-7234	11	4	image	image	NOUN
fcis-7234	11	5	quality	quality	NOUN
fcis-7234	11	6	assessment	assessment	NOUN
fcis-7234	11	7	is	be	AUX
fcis-7234	11	8	an	an	DET
fcis-7234	11	9	algorithm	algorithm	NOUN
fcis-7234	11	10	that	that	PRON
fcis-7234	11	11	evaluates	evaluate	VERB
fcis-7234	11	12	the	the	DET
fcis-7234	11	13	quality	quality	NOUN
fcis-7234	11	14	of	of	ADP
fcis-7234	11	15	an	an	DET
fcis-7234	11	16	image	image	NOUN
fcis-7234	11	17	by	by	ADP
fcis-7234	11	18	using	use	VERB
fcis-7234	11	19	only	only	ADV
fcis-7234	11	20	an	an	DET
fcis-7234	11	21	image	image	NOUN
fcis-7234	11	22	itself	itself	PRON
fcis-7234	11	23	.	.	PUNCT
fcis-7234	12	1	this	this	DET
fcis-7234	12	2	algorithm	algorithm	NOUN
fcis-7234	12	3	is	be	AUX
fcis-7234	12	4	usually	usually	ADV
fcis-7234	12	5	used	use	VERB
fcis-7234	12	6	for	for	ADP
fcis-7234	12	7	automatic	automatic	ADJ
fcis-7234	12	8	image	image	NOUN
fcis-7234	12	9	quality	quality	NOUN
fcis-7234	12	10	assessment	assessment	NOUN
fcis-7234	12	11	without	without	ADP
fcis-7234	12	12	reference	reference	NOUN
fcis-7234	12	13	images	image	NOUN
fcis-7234	12	14	or	or	CCONJ
fcis-7234	12	15	human	human	ADJ
fcis-7234	12	16	subjective	subjective	ADJ
fcis-7234	12	17	involvement	involvement	NOUN
fcis-7234	12	18	.	.	PUNCT
fcis-7234	13	1	therefore	therefore	ADV
fcis-7234	13	2	,	,	PUNCT
fcis-7234	13	3	the	the	DET
fcis-7234	13	4	application	application	NOUN
fcis-7234	13	5	of	of	ADP
fcis-7234	13	6	this	this	DET
fcis-7234	13	7	algorithm	algorithm	NOUN
fcis-7234	13	8	on	on	ADP
fcis-7234	13	9	large	large	ADJ
fcis-7234	13	10	-	-	PUNCT
fcis-7234	13	11	scale	scale	NOUN
fcis-7234	13	12	image	image	NOUN
fcis-7234	13	13	databases	database	NOUN
fcis-7234	13	14	is	be	AUX
fcis-7234	13	15	of	of	ADP
fcis-7234	13	16	great	great	ADJ
fcis-7234	13	17	practical	practical	ADJ
fcis-7234	13	18	value	value	NOUN
fcis-7234	13	19	.	.	PUNCT
fcis-7234	14	1	because	because	SCONJ
fcis-7234	14	2	there	there	PRON
fcis-7234	14	3	is	be	VERB
fcis-7234	14	4	no	no	DET
fcis-7234	14	5	reference	reference	NOUN
fcis-7234	14	6	image	image	NOUN
fcis-7234	14	7	as	as	SCONJ
fcis-7234	14	8	a	a	DET
fcis-7234	14	9	comparison	comparison	NOUN
fcis-7234	14	10	standard	standard	NOUN
fcis-7234	14	11	,	,	PUNCT
fcis-7234	14	12	nr	nr	PROPN
fcis-7234	14	13	-	-	PUNCT
fcis-7234	14	14	iqa	iqa	PROPN
fcis-7234	14	15	methods	method	NOUN
fcis-7234	14	16	can	can	AUX
fcis-7234	14	17	only	only	ADV
fcis-7234	14	18	use	use	VERB
fcis-7234	14	19	distorted	distort	VERB
fcis-7234	14	20	images	image	NOUN
fcis-7234	14	21	to	to	PART
fcis-7234	14	22	extract	extract	VERB
fcis-7234	14	23	the	the	DET
fcis-7234	14	24	statistical	statistical	ADJ
fcis-7234	14	25	features	feature	NOUN
fcis-7234	14	26	of	of	ADP
fcis-7234	14	27	distortions	distortion	NOUN
fcis-7234	14	28	caused	cause	VERB
fcis-7234	14	29	by	by	ADP
fcis-7234	14	30	distortion	distortion	NOUN
fcis-7234	14	31	.	.	PUNCT
fcis-7234	15	1	among	among	ADP
fcis-7234	15	2	the	the	DET
fcis-7234	15	3	nr	nr	PROPN
fcis-7234	15	4	-	-	PROPN
fcis-7234	15	5	iqa	iqa	PROPN
fcis-7234	15	6	methods	method	NOUN
fcis-7234	15	7	based	base	VERB
fcis-7234	15	8	on	on	ADP
fcis-7234	15	9	feature	feature	NOUN
fcis-7234	15	10	extraction	extraction	NOUN
fcis-7234	15	11	,	,	PUNCT
fcis-7234	15	12	they	they	PRON
fcis-7234	15	13	can	can	AUX
fcis-7234	15	14	be	be	AUX
fcis-7234	15	15	broadly	broadly	ADV
fcis-7234	15	16	classified	classify	VERB
fcis-7234	15	17	into	into	ADP
fcis-7234	15	18	two	two	NUM
fcis-7234	15	19	categories	category	NOUN
fcis-7234	15	20	:	:	PUNCT
fcis-7234	15	21	natural	natural	ADJ
fcis-7234	15	22	statistical	statistical	ADJ
fcis-7234	15	23	feature	feature	NOUN
fcis-7234	15	24	-	-	PUNCT
fcis-7234	15	25	based	base	VERB
fcis-7234	15	26	methods	method	NOUN
fcis-7234	15	27	and	and	CCONJ
fcis-7234	15	28	feature	feature	NOUN
fcis-7234	15	29	learningbased	learningbase	VERB
fcis-7234	15	30	methods	method	NOUN
fcis-7234	15	31	.	.	PUNCT
fcis-7234	16	1	however	however	ADV
fcis-7234	16	2	,	,	PUNCT
fcis-7234	16	3	variations	variation	NOUN
fcis-7234	16	4	of	of	ADP
fcis-7234	16	5	natural	natural	ADJ
fcis-7234	16	6	statistical	statistical	ADJ
fcis-7234	16	7	features	feature	NOUN
fcis-7234	16	8	may	may	AUX
fcis-7234	16	9	be	be	AUX
fcis-7234	16	10	presented	present	VERB
fcis-7234	16	11	in	in	ADP
fcis-7234	16	12	multiple	multiple	ADJ
fcis-7234	16	13	ways	way	NOUN
fcis-7234	16	14	.	.	PUNCT
fcis-7234	17	1	for	for	ADP
fcis-7234	17	2	example	example	NOUN
fcis-7234	17	3	,	,	PUNCT
fcis-7234	17	4	in	in	ADP
fcis-7234	17	5	the	the	DET
fcis-7234	17	6	literature	literature	NOUN
fcis-7234	17	7	[	[	X
fcis-7234	17	8	1	1	NUM
fcis-7234	17	9	]	]	PUNCT
fcis-7234	17	10	,	,	PUNCT
fcis-7234	17	11	the	the	DET
fcis-7234	17	12	main	main	ADJ
fcis-7234	17	13	objective	objective	NOUN
fcis-7234	17	14	of	of	ADP
fcis-7234	17	15	this	this	DET
fcis-7234	17	16	paper	paper	NOUN
fcis-7234	17	17	is	be	AUX
fcis-7234	17	18	to	to	PART
fcis-7234	17	19	propose	propose	VERB
fcis-7234	17	20	a	a	DET
fcis-7234	17	21	reference	reference	NOUN
fcis-7234	17	22	-	-	PUNCT
fcis-7234	17	23	free	free	ADJ
fcis-7234	17	24	image	image	NOUN
fcis-7234	17	25	quality	quality	NOUN
fcis-7234	17	26	assessment	assessment	NOUN
fcis-7234	17	27	metric	metric	NOUN
fcis-7234	17	28	based	base	VERB
fcis-7234	17	29	on	on	ADP
fcis-7234	17	30	regional	regional	ADJ
fcis-7234	17	31	mutual	mutual	ADJ
fcis-7234	17	32	information	information	NOUN
fcis-7234	17	33	,	,	PUNCT
fcis-7234	17	34	which	which	PRON
fcis-7234	17	35	evaluates	evaluate	VERB
fcis-7234	17	36	the	the	DET
fcis-7234	17	37	quality	quality	NOUN
fcis-7234	17	38	of	of	ADP
fcis-7234	17	39	images	image	NOUN
fcis-7234	17	40	based	base	VERB
fcis-7234	17	41	on	on	ADP
fcis-7234	17	42	mutual	mutual	ADJ
fcis-7234	17	43	information	information	NOUN
fcis-7234	17	44	within	within	ADP
fcis-7234	17	45	different	different	ADJ
fcis-7234	17	46	regions	region	NOUN
fcis-7234	17	47	and	and	CCONJ
fcis-7234	17	48	solves	solve	VERB
fcis-7234	17	49	the	the	DET
fcis-7234	17	50	problem	problem	NOUN
fcis-7234	17	51	that	that	SCONJ
fcis-7234	17	52	traditional	traditional	ADJ
fcis-7234	17	53	reference	reference	NOUN
fcis-7234	17	54	-	-	PUNCT
fcis-7234	17	55	free	free	ADJ
fcis-7234	17	56	image	image	NOUN
fcis-7234	17	57	quality	quality	NOUN
fcis-7234	17	58	assessment	assessment	NOUN
fcis-7234	17	59	metrics	metric	NOUN
fcis-7234	17	60	are	be	AUX
fcis-7234	17	61	difficult	difficult	ADJ
fcis-7234	17	62	to	to	PART
fcis-7234	17	63	obtain	obtain	VERB
fcis-7234	17	64	high	high	ADJ
fcis-7234	17	65	accuracy	accuracy	NOUN
fcis-7234	17	66	.	.	PUNCT
fcis-7234	18	1	oszust	oszust	ADJ
fcis-7234	19	1	[	[	X
fcis-7234	19	2	2	2	NUM
fcis-7234	19	3	]	]	PUNCT
fcis-7234	19	4	introduces	introduce	NOUN
fcis-7234	19	5	a	a	DET
fcis-7234	19	6	new	new	ADJ
fcis-7234	19	7	method	method	NOUN
fcis-7234	19	8	for	for	ADP
fcis-7234	19	9	blind	blind	ADJ
fcis-7234	19	10	image	image	NOUN
fcis-7234	19	11	quality	quality	NOUN
fcis-7234	19	12	assessment	assessment	NOUN
fcis-7234	19	13	that	that	PRON
fcis-7234	19	14	combines	combine	VERB
fcis-7234	19	15	local	local	ADJ
fcis-7234	19	16	feature	feature	NOUN
fcis-7234	19	17	descriptors	descriptor	NOUN
fcis-7234	19	18	and	and	CCONJ
fcis-7234	19	19	derivative	derivative	ADJ
fcis-7234	19	20	filters	filter	NOUN
fcis-7234	19	21	,	,	PUNCT
fcis-7234	19	22	firstly	firstly	ADV
fcis-7234	19	23	,	,	PUNCT
fcis-7234	19	24	by	by	ADP
fcis-7234	19	25	extracting	extract	VERB
fcis-7234	19	26	and	and	CCONJ
fcis-7234	19	27	matching	match	VERB
fcis-7234	19	28	the	the	DET
fcis-7234	19	29	feature	feature	NOUN
fcis-7234	19	30	points	point	NOUN
fcis-7234	19	31	in	in	ADP
fcis-7234	19	32	the	the	DET
fcis-7234	19	33	reference	reference	NOUN
fcis-7234	19	34	image	image	NOUN
fcis-7234	19	35	and	and	CCONJ
fcis-7234	19	36	the	the	DET
fcis-7234	19	37	image	image	NOUN
fcis-7234	19	38	to	to	PART
fcis-7234	19	39	be	be	AUX
fcis-7234	19	40	evaluated	evaluate	VERB
fcis-7234	19	41	,	,	PUNCT
fcis-7234	19	42	and	and	CCONJ
fcis-7234	19	43	calculating	calculate	VERB
fcis-7234	19	44	the	the	DET
fcis-7234	19	45	variation	variation	NOUN
fcis-7234	19	46	between	between	ADP
fcis-7234	19	47	them	they	PRON
fcis-7234	19	48	.	.	PUNCT
fcis-7234	20	1	then	then	ADV
fcis-7234	20	2	,	,	PUNCT
fcis-7234	20	3	the	the	DET
fcis-7234	20	4	gradient	gradient	ADJ
fcis-7234	20	5	information	information	NOUN
fcis-7234	20	6	is	be	AUX
fcis-7234	20	7	extracted	extract	VERB
fcis-7234	20	8	from	from	ADP
fcis-7234	20	9	the	the	DET
fcis-7234	20	10	image	image	NOUN
fcis-7234	20	11	using	use	VERB
fcis-7234	20	12	the	the	DET
fcis-7234	20	13	derivative	derivative	ADJ
fcis-7234	20	14	filter	filter	NOUN
fcis-7234	20	15	,	,	PUNCT
fcis-7234	20	16	and	and	CCONJ
fcis-7234	20	17	the	the	DET
fcis-7234	20	18	quality	quality	NOUN
fcis-7234	20	19	of	of	ADP
fcis-7234	20	20	the	the	DET
fcis-7234	20	21	image	image	NOUN
fcis-7234	20	22	is	be	AUX
fcis-7234	20	23	evaluated	evaluate	VERB
fcis-7234	20	24	by	by	ADP
fcis-7234	20	25	feature	feature	NOUN
fcis-7234	20	26	point	point	NOUN
fcis-7234	20	27	matching	matching	NOUN
fcis-7234	20	28	.	.	PUNCT
fcis-7234	21	1	the	the	DET
fcis-7234	21	2	literature	literature	NOUN
fcis-7234	21	3	[	[	X
fcis-7234	21	4	3	3	X
fcis-7234	21	5	]	]	PUNCT
fcis-7234	21	6	proposes	propose	VERB
fcis-7234	21	7	an	an	DET
fcis-7234	21	8	unsupervised	unsupervised	ADJ
fcis-7234	21	9	blind	blind	ADJ
fcis-7234	21	10	vision	vision	NOUN
fcis-7234	21	11	quality	quality	NOUN
fcis-7234	21	12	assessment	assessment	NOUN
fcis-7234	21	13	method	method	NOUN
fcis-7234	21	14	based	base	VERB
fcis-7234	21	15	on	on	ADP
fcis-7234	21	16	three	three	NUM
fcis-7234	21	17	key	key	ADJ
fcis-7234	21	18	factors	factor	NOUN
fcis-7234	21	19	:	:	PUNCT
fcis-7234	21	20	structure	structure	NOUN
fcis-7234	21	21	,	,	PUNCT
fcis-7234	21	22	naturalness	naturalness	NOUN
fcis-7234	21	23	and	and	CCONJ
fcis-7234	21	24	perception	perception	NOUN
fcis-7234	21	25	.	.	PUNCT
fcis-7234	22	1	the	the	DET
fcis-7234	22	2	image	image	NOUN
fcis-7234	22	3	is	be	AUX
fcis-7234	22	4	first	first	ADV
fcis-7234	22	5	segmented	segment	VERB
fcis-7234	22	6	,	,	PUNCT
fcis-7234	22	7	and	and	CCONJ
fcis-7234	22	8	then	then	ADV
fcis-7234	22	9	the	the	DET
fcis-7234	22	10	structure	structure	NOUN
fcis-7234	22	11	,	,	PUNCT
fcis-7234	22	12	naturalness	naturalness	NOUN
fcis-7234	22	13	and	and	CCONJ
fcis-7234	22	14	perceptual	perceptual	ADJ
fcis-7234	22	15	features	feature	NOUN
fcis-7234	22	16	of	of	ADP
fcis-7234	22	17	each	each	DET
fcis-7234	22	18	region	region	NOUN
fcis-7234	22	19	after	after	ADP
fcis-7234	22	20	segmentation	segmentation	NOUN
fcis-7234	22	21	are	be	AUX
fcis-7234	22	22	analyzed	analyze	VERB
fcis-7234	22	23	.	.	PUNCT
fcis-7234	23	1	then	then	ADV
fcis-7234	23	2	,	,	PUNCT
fcis-7234	23	3	the	the	DET
fcis-7234	23	4	quality	quality	NOUN
fcis-7234	23	5	score	score	NOUN
fcis-7234	23	6	of	of	ADP
fcis-7234	23	7	each	each	DET
fcis-7234	23	8	region	region	NOUN
fcis-7234	23	9	is	be	AUX
fcis-7234	23	10	calculated	calculate	VERB
fcis-7234	23	11	based	base	VERB
fcis-7234	23	12	on	on	ADP
fcis-7234	23	13	these	these	DET
fcis-7234	23	14	features	feature	NOUN
fcis-7234	23	15	.	.	PUNCT
fcis-7234	24	1	the	the	DET
fcis-7234	24	2	assumption	assumption	NOUN
fcis-7234	24	3	underlying	underlie	VERB
fcis-7234	24	4	all	all	DET
fcis-7234	24	5	these	these	DET
fcis-7234	24	6	algorithms	algorithm	NOUN
fcis-7234	24	7	is	be	AUX
fcis-7234	24	8	that	that	DET
fcis-7234	24	9	distortion	distortion	NOUN
fcis-7234	24	10	affects	affect	VERB
fcis-7234	24	11	certain	certain	ADJ
fcis-7234	24	12	statistical	statistical	ADJ
fcis-7234	24	13	features	feature	NOUN
fcis-7234	24	14	of	of	ADP
fcis-7234	24	15	natural	natural	ADJ
fcis-7234	24	16	images	image	NOUN
fcis-7234	24	17	,	,	PUNCT
fcis-7234	24	18	but	but	CCONJ
fcis-7234	24	19	these	these	DET
fcis-7234	24	20	features	feature	NOUN
fcis-7234	24	21	are	be	AUX
fcis-7234	24	22	difficult	difficult	ADJ
fcis-7234	24	23	to	to	PART
fcis-7234	24	24	determine	determine	VERB
fcis-7234	24	25	comprehensively	comprehensively	ADV
fcis-7234	24	26	and	and	CCONJ
fcis-7234	24	27	accurately	accurately	ADV
fcis-7234	24	28	by	by	ADP
fcis-7234	24	29	manual	manual	ADJ
fcis-7234	24	30	means	mean	NOUN
fcis-7234	24	31	.	.	PUNCT
fcis-7234	25	1	the	the	DET
fcis-7234	25	2	literature	literature	NOUN
fcis-7234	25	3	[	[	X
fcis-7234	25	4	4	4	NUM
fcis-7234	25	5	]	]	PUNCT
fcis-7234	25	6	focuses	focus	VERB
fcis-7234	25	7	on	on	ADP
fcis-7234	25	8	reference	reference	NOUN
fcis-7234	25	9	-	-	PUNCT
fcis-7234	25	10	free	free	ADJ
fcis-7234	25	11	and	and	CCONJ
fcis-7234	25	12	full	full	ADJ
fcis-7234	25	13	-	-	PUNCT
fcis-7234	25	14	reference	reference	NOUN
fcis-7234	25	15	image	image	NOUN
fcis-7234	25	16	quality	quality	NOUN
fcis-7234	25	17	assessment	assessment	NOUN
fcis-7234	25	18	using	use	VERB
fcis-7234	25	19	deep	deep	ADJ
fcis-7234	25	20	neural	neural	ADJ
fcis-7234	25	21	networks	network	NOUN
fcis-7234	25	22	,	,	PUNCT
fcis-7234	25	23	proposing	propose	VERB
fcis-7234	25	24	two	two	NUM
fcis-7234	25	25	deep	deep	ADJ
fcis-7234	25	26	neural	neural	ADJ
fcis-7234	25	27	network	network	NOUN
fcis-7234	25	28	models	model	NOUN
fcis-7234	25	29	:	:	PUNCT
fcis-7234	25	30	one	one	NUM
fcis-7234	25	31	for	for	ADP
fcis-7234	25	32	reference	reference	NOUN
fcis-7234	25	33	-	-	PUNCT
fcis-7234	25	34	free	free	ADJ
fcis-7234	25	35	assessment	assessment	NOUN
fcis-7234	25	36	and	and	CCONJ
fcis-7234	25	37	the	the	DET
fcis-7234	25	38	other	other	ADJ
fcis-7234	25	39	for	for	ADP
fcis-7234	25	40	full	full	ADJ
fcis-7234	25	41	-	-	PUNCT
fcis-7234	25	42	reference	reference	NOUN
fcis-7234	25	43	assessment	assessment	NOUN
fcis-7234	25	44	.	.	PUNCT
fcis-7234	26	1	le	le	PROPN
fcis-7234	26	2	kang	kang	PROPN
fcis-7234	26	3	et	et	PROPN
fcis-7234	26	4	al	al	PROPN
fcis-7234	27	1	[	[	X
fcis-7234	27	2	5	5	NUM
fcis-7234	27	3	]	]	PUNCT
fcis-7234	27	4	proposed	propose	VERB
fcis-7234	27	5	a	a	DET
fcis-7234	27	6	reference	reference	NOUN
fcis-7234	27	7	-	-	PUNCT
fcis-7234	27	8	free	free	ADJ
fcis-7234	27	9	image	image	NOUN
fcis-7234	27	10	quality	quality	NOUN
fcis-7234	27	11	assessment	assessment	NOUN
fcis-7234	27	12	model	model	NOUN
fcis-7234	27	13	using	use	VERB
fcis-7234	27	14	convolutional	convolutional	ADJ
fcis-7234	27	15	neural	neural	ADJ
fcis-7234	27	16	networks	network	NOUN
fcis-7234	27	17	.	.	PUNCT
fcis-7234	28	1	this	this	DET
fcis-7234	28	2	model	model	NOUN
fcis-7234	28	3	makes	make	VERB
fcis-7234	28	4	it	it	PRON
fcis-7234	28	5	possible	possible	ADJ
fcis-7234	28	6	to	to	PART
fcis-7234	28	7	assess	assess	VERB
fcis-7234	28	8	image	image	NOUN
fcis-7234	28	9	quality	quality	NOUN
fcis-7234	28	10	without	without	ADP
fcis-7234	28	11	the	the	DET
fcis-7234	28	12	need	need	NOUN
fcis-7234	28	13	for	for	ADP
fcis-7234	28	14	reference	reference	NOUN
fcis-7234	28	15	images	image	NOUN
fcis-7234	28	16	or	or	CCONJ
fcis-7234	28	17	specialized	specialized	ADJ
fcis-7234	28	18	training	training	NOUN
fcis-7234	28	19	datasets	dataset	NOUN
fcis-7234	28	20	and	and	CCONJ
fcis-7234	28	21	allows	allow	VERB
fcis-7234	28	22	accurate	accurate	ADJ
fcis-7234	28	23	assessment	assessment	NOUN
fcis-7234	28	24	under	under	ADP
fcis-7234	28	25	different	different	ADJ
fcis-7234	28	26	types	type	NOUN
fcis-7234	28	27	of	of	ADP
fcis-7234	28	28	distortions	distortion	NOUN
fcis-7234	28	29	.	.	PUNCT
fcis-7234	29	1	the	the	DET
fcis-7234	29	2	authors	author	NOUN
fcis-7234	29	3	demonstrated	demonstrate	VERB
fcis-7234	29	4	the	the	DET
fcis-7234	29	5	accuracy	accuracy	NOUN
fcis-7234	29	6	and	and	CCONJ
fcis-7234	29	7	robustness	robustness	NOUN
fcis-7234	29	8	of	of	ADP
fcis-7234	29	9	the	the	DET
fcis-7234	29	10	model	model	NOUN
fcis-7234	29	11	by	by	ADP
fcis-7234	29	12	testing	test	VERB
fcis-7234	29	13	it	it	PRON
fcis-7234	29	14	against	against	ADP
fcis-7234	29	15	several	several	ADJ
fcis-7234	29	16	different	different	ADJ
fcis-7234	29	17	types	type	NOUN
fcis-7234	29	18	of	of	ADP
fcis-7234	29	19	image	image	NOUN
fcis-7234	29	20	distortions	distortion	NOUN
fcis-7234	29	21	such	such	ADJ
fcis-7234	29	22	as	as	ADP
fcis-7234	29	23	blur	blur	NOUN
fcis-7234	29	24	,	,	PUNCT
fcis-7234	29	25	noise	noise	NOUN
fcis-7234	29	26	,	,	PUNCT
fcis-7234	29	27	and	and	CCONJ
fcis-7234	29	28	compression	compression	NOUN
fcis-7234	29	29	.	.	PUNCT
fcis-7234	30	1	simone	simone	PROPN
fcis-7234	30	2	bianco	bianco	PROPN
fcis-7234	30	3	et	et	PROPN
fcis-7234	30	4	al	al	PROPN
fcis-7234	31	1	[	[	X
fcis-7234	31	2	6	6	NUM
fcis-7234	31	3	]	]	PUNCT
fcis-7234	31	4	proposed	propose	VERB
fcis-7234	31	5	a	a	DET
fcis-7234	31	6	blind	blind	ADJ
fcis-7234	31	7	image	image	NOUN
fcis-7234	31	8	quality	quality	NOUN
fcis-7234	31	9	assessment	assessment	NOUN
fcis-7234	31	10	method	method	NOUN
fcis-7234	31	11	based	base	VERB
fcis-7234	31	12	on	on	ADP
fcis-7234	31	13	convolutional	convolutional	ADJ
fcis-7234	31	14	neural	neural	ADJ
fcis-7234	31	15	networks	network	NOUN
fcis-7234	31	16	,	,	PUNCT
fcis-7234	31	17	where	where	SCONJ
fcis-7234	31	18	features	feature	NOUN
fcis-7234	31	19	are	be	AUX
fcis-7234	31	20	extracted	extract	VERB
fcis-7234	31	21	from	from	ADP
fcis-7234	31	22	the	the	DET
fcis-7234	31	23	original	original	ADJ
fcis-7234	31	24	image	image	NOUN
fcis-7234	31	25	using	use	VERB
fcis-7234	31	26	a	a	DET
fcis-7234	31	27	cnn	cnn	NOUN
fcis-7234	31	28	and	and	CCONJ
fcis-7234	31	29	these	these	DET
fcis-7234	31	30	features	feature	NOUN
fcis-7234	31	31	are	be	AUX
fcis-7234	31	32	fed	feed	VERB
fcis-7234	31	33	into	into	ADP
fcis-7234	31	34	a	a	DET
fcis-7234	31	35	support	support	NOUN
fcis-7234	31	36	vector	vector	NOUN
fcis-7234	31	37	regression	regression	NOUN
fcis-7234	31	38	(	(	PUNCT
fcis-7234	31	39	svr	svr	PROPN
fcis-7234	31	40	)	)	PUNCT
fcis-7234	31	41	model	model	NOUN
fcis-7234	31	42	in	in	ADP
fcis-7234	31	43	which	which	PRON
fcis-7234	31	44	quality	quality	NOUN
fcis-7234	31	45	assessment	assessment	NOUN
fcis-7234	31	46	is	be	AUX
fcis-7234	31	47	performed	perform	VERB
fcis-7234	31	48	.	.	PUNCT
fcis-7234	32	1	and	and	CCONJ
fcis-7234	32	2	the	the	DET
fcis-7234	32	3	emergence	emergence	NOUN
fcis-7234	32	4	of	of	ADP
fcis-7234	32	5	gan	gan	PROPN
fcis-7234	32	6	has	have	AUX
fcis-7234	32	7	injected	inject	VERB
fcis-7234	32	8	new	new	ADJ
fcis-7234	32	9	ideas	idea	NOUN
fcis-7234	32	10	into	into	ADP
fcis-7234	32	11	iqa	iqa	PROPN
fcis-7234	32	12	.	.	PUNCT
fcis-7234	33	1	a	a	DET
fcis-7234	33	2	novel	novel	ADJ
fcis-7234	33	3	reference	reference	NOUN
fcis-7234	33	4	-	-	PUNCT
fcis-7234	33	5	free	free	ADJ
fcis-7234	33	6	image	image	NOUN
fcis-7234	33	7	quality	quality	NOUN
fcis-7234	33	8	assessment	assessment	NOUN
fcis-7234	33	9	method	method	NOUN
fcis-7234	33	10	,	,	PUNCT
fcis-7234	33	11	called	call	VERB
fcis-7234	33	12	hallucinated	hallucinate	VERB
fcis-7234	33	13	-	-	PUNCT
fcis-7234	33	14	iqa	iqa	PROPN
fcis-7234	33	15	,	,	PUNCT
fcis-7234	33	16	was	be	AUX
fcis-7234	33	17	proposed	propose	VERB
fcis-7234	33	18	in	in	ADP
fcis-7234	33	19	the	the	DET
fcis-7234	33	20	literature	literature	NOUN
fcis-7234	33	21	[	[	X
fcis-7234	33	22	7	7	NUM
fcis-7234	33	23	]	]	PUNCT
fcis-7234	33	24	.	.	PUNCT
fcis-7234	34	1	this	this	DET
fcis-7234	34	2	method	method	NOUN
fcis-7234	34	3	utilizes	utilize	VERB
fcis-7234	34	4	generative	generative	ADJ
fcis-7234	34	5	adversarial	adversarial	ADJ
fcis-7234	34	6	networks	network	NOUN
fcis-7234	34	7	(	(	PUNCT
fcis-7234	34	8	gan	gan	PROPN
fcis-7234	34	9	)	)	PUNCT
fcis-7234	34	10	to	to	PART
fcis-7234	34	11	learn	learn	VERB
fcis-7234	34	12	the	the	DET
fcis-7234	34	13	implicit	implicit	ADJ
fcis-7234	34	14	representation	representation	NOUN
fcis-7234	34	15	and	and	CCONJ
fcis-7234	34	16	quality	quality	NOUN
fcis-7234	34	17	features	feature	NOUN
fcis-7234	34	18	of	of	ADP
fcis-7234	34	19	an	an	DET
fcis-7234	34	20	image	image	NOUN
fcis-7234	34	21	,	,	PUNCT
fcis-7234	34	22	learn	learn	VERB
fcis-7234	34	23	the	the	DET
fcis-7234	34	24	conversion	conversion	NOUN
fcis-7234	34	25	from	from	ADP
fcis-7234	34	26	lowquality	lowquality	NOUN
fcis-7234	34	27	to	to	ADP
fcis-7234	34	28	high	high	ADJ
fcis-7234	34	29	-	-	PUNCT
fcis-7234	34	30	quality	quality	NOUN
fcis-7234	34	31	images	image	NOUN
fcis-7234	34	32	,	,	PUNCT
fcis-7234	34	33	and	and	CCONJ
fcis-7234	34	34	thus	thus	ADV
fcis-7234	34	35	assess	assess	VERB
fcis-7234	34	36	image	image	NOUN
fcis-7234	34	37	quality	quality	NOUN
fcis-7234	34	38	.	.	PUNCT
fcis-7234	35	1	a	a	DET
fcis-7234	35	2	new	new	ADJ
fcis-7234	35	3	reference	reference	NOUN
fcis-7234	35	4	-	-	PUNCT
fcis-7234	35	5	free	free	ADJ
fcis-7234	35	6	image	image	NOUN
fcis-7234	35	7	quality	quality	NOUN
fcis-7234	35	8	assessment	assessment	NOUN
fcis-7234	35	9	method	method	NOUN
fcis-7234	35	10	,	,	PUNCT
fcis-7234	35	11	called	call	VERB
fcis-7234	35	12	ran4iqa	ran4iqa	NOUN
fcis-7234	35	13	,	,	PUNCT
fcis-7234	35	14	was	be	AUX
fcis-7234	35	15	proposed	propose	VERB
fcis-7234	35	16	in	in	ADP
fcis-7234	35	17	the	the	DET
fcis-7234	35	18	literature	literature	NOUN
fcis-7234	35	19	[	[	X
fcis-7234	35	20	8	8	NUM
fcis-7234	35	21	]	]	PUNCT
fcis-7234	35	22	,	,	PUNCT
fcis-7234	35	23	which	which	PRON
fcis-7234	35	24	combines	combine	VERB
fcis-7234	35	25	deep	deep	ADJ
fcis-7234	35	26	convolutional	convolutional	ADJ
fcis-7234	35	27	neural	neural	ADJ
fcis-7234	35	28	networks	network	NOUN
fcis-7234	35	29	and	and	CCONJ
fcis-7234	35	30	generative	generative	ADJ
fcis-7234	35	31	adversarial	adversarial	ADJ
fcis-7234	35	32	networks	network	NOUN
fcis-7234	35	33	to	to	PART
fcis-7234	35	34	achieve	achieve	VERB
fcis-7234	35	35	the	the	DET
fcis-7234	35	36	assessment	assessment	NOUN
fcis-7234	35	37	of	of	ADP
fcis-7234	35	38	image	image	NOUN
fcis-7234	35	39	quality	quality	NOUN
fcis-7234	35	40	by	by	ADP
fcis-7234	35	41	learning	learn	VERB
fcis-7234	35	42	loss	loss	NOUN
fcis-7234	35	43	functions	function	NOUN
fcis-7234	35	44	.	.	PUNCT
fcis-7234	36	1	in	in	ADP
fcis-7234	36	2	the	the	DET
fcis-7234	36	3	literature	literature	NOUN
fcis-7234	36	4	[	[	X
fcis-7234	36	5	9	9	NUM
fcis-7234	36	6	]	]	PUNCT
fcis-7234	36	7	,	,	PUNCT
fcis-7234	36	8	the	the	DET
fcis-7234	36	9	59	59	NUM
fcis-7234	36	10	authors	author	NOUN
fcis-7234	36	11	proposed	propose	VERB
fcis-7234	36	12	a	a	DET
fcis-7234	36	13	new	new	ADJ
fcis-7234	36	14	blind	blind	ADJ
fcis-7234	36	15	image	image	NOUN
fcis-7234	36	16	quality	quality	NOUN
fcis-7234	36	17	assessment	assessment	NOUN
fcis-7234	36	18	(	(	PUNCT
fcis-7234	36	19	biqa	biqa	ADJ
fcis-7234	36	20	)	)	PUNCT
fcis-7234	36	21	method	method	NOUN
fcis-7234	36	22	,	,	PUNCT
fcis-7234	36	23	which	which	PRON
fcis-7234	36	24	aims	aim	VERB
fcis-7234	36	25	to	to	PART
fcis-7234	36	26	generate	generate	VERB
fcis-7234	36	27	an	an	DET
fcis-7234	36	28	image	image	NOUN
fcis-7234	36	29	with	with	ADP
fcis-7234	36	30	similar	similar	ADJ
fcis-7234	36	31	visual	visual	ADJ
fcis-7234	36	32	quality	quality	NOUN
fcis-7234	36	33	to	to	ADP
fcis-7234	36	34	the	the	DET
fcis-7234	36	35	input	input	NOUN
fcis-7234	36	36	image	image	NOUN
fcis-7234	36	37	.	.	PUNCT
fcis-7234	37	1	in	in	ADP
fcis-7234	37	2	this	this	DET
fcis-7234	37	3	paper[10	paper[10	NOUN
fcis-7234	37	4	]	]	PUNCT
fcis-7234	37	5	,	,	PUNCT
fcis-7234	37	6	a	a	DET
fcis-7234	37	7	reference	reference	NOUN
fcis-7234	37	8	-	-	PUNCT
fcis-7234	37	9	free	free	ADJ
fcis-7234	37	10	image	image	NOUN
fcis-7234	37	11	quality	quality	NOUN
fcis-7234	37	12	assessment	assessment	NOUN
fcis-7234	37	13	algorithm	algorithm	NOUN
fcis-7234	37	14	based	base	VERB
fcis-7234	37	15	on	on	ADP
fcis-7234	37	16	enhanced	enhance	VERB
fcis-7234	37	17	adversarial	adversarial	ADJ
fcis-7234	37	18	learning	learning	NOUN
fcis-7234	37	19	is	be	AUX
fcis-7234	37	20	proposed	propose	VERB
fcis-7234	37	21	to	to	PART
fcis-7234	37	22	improve	improve	VERB
fcis-7234	37	23	the	the	DET
fcis-7234	37	24	strength	strength	NOUN
fcis-7234	37	25	of	of	ADP
fcis-7234	37	26	the	the	DET
fcis-7234	37	27	adversarial	adversarial	ADJ
fcis-7234	37	28	learning	learning	NOUN
fcis-7234	37	29	by	by	ADP
fcis-7234	37	30	improving	improve	VERB
fcis-7234	37	31	the	the	DET
fcis-7234	37	32	loss	loss	NOUN
fcis-7234	37	33	function	function	NOUN
fcis-7234	37	34	and	and	CCONJ
fcis-7234	37	35	the	the	DET
fcis-7234	37	36	structure	structure	NOUN
fcis-7234	37	37	of	of	ADP
fcis-7234	37	38	the	the	DET
fcis-7234	37	39	network	network	NOUN
fcis-7234	37	40	model	model	NOUN
fcis-7234	37	41	,	,	PUNCT
fcis-7234	37	42	and	and	CCONJ
fcis-7234	37	43	to	to	PART
fcis-7234	37	44	output	output	VERB
fcis-7234	37	45	a	a	DET
fcis-7234	37	46	more	more	ADV
fcis-7234	37	47	reliable	reliable	ADJ
fcis-7234	37	48	simulation	simulation	NOUN
fcis-7234	37	49	"	"	PUNCT
fcis-7234	37	50	reference	reference	NOUN
fcis-7234	37	51	image	image	NOUN
fcis-7234	37	52	"	"	PUNCT
fcis-7234	37	53	to	to	PART
fcis-7234	37	54	achieve	achieve	VERB
fcis-7234	37	55	similar	similar	ADJ
fcis-7234	37	56	results	result	NOUN
fcis-7234	37	57	as	as	ADP
fcis-7234	37	58	the	the	DET
fcis-7234	37	59	full	full	ADJ
fcis-7234	37	60	-	-	PUNCT
fcis-7234	37	61	reference	reference	NOUN
fcis-7234	37	62	image	image	NOUN
fcis-7234	37	63	quality	quality	NOUN
fcis-7234	37	64	assessment	assessment	NOUN
fcis-7234	37	65	method	method	NOUN
fcis-7234	37	66	.	.	PUNCT
fcis-7234	38	1	in	in	ADP
fcis-7234	38	2	order	order	NOUN
fcis-7234	38	3	to	to	PART
fcis-7234	38	4	improve	improve	VERB
fcis-7234	38	5	the	the	DET
fcis-7234	38	6	accuracy	accuracy	NOUN
fcis-7234	38	7	of	of	ADP
fcis-7234	38	8	the	the	DET
fcis-7234	38	9	images	image	NOUN
fcis-7234	38	10	output	output	NOUN
fcis-7234	38	11	from	from	ADP
fcis-7234	38	12	the	the	DET
fcis-7234	38	13	generative	generative	ADJ
fcis-7234	38	14	adversarial	adversarial	ADJ
fcis-7234	38	15	network	network	NOUN
fcis-7234	38	16	,	,	PUNCT
fcis-7234	38	17	the	the	DET
fcis-7234	38	18	prediction	prediction	NOUN
fcis-7234	38	19	accuracy	accuracy	NOUN
fcis-7234	38	20	and	and	CCONJ
fcis-7234	38	21	generalization	generalization	NOUN
fcis-7234	38	22	of	of	ADP
fcis-7234	38	23	the	the	DET
fcis-7234	38	24	image	image	NOUN
fcis-7234	38	25	quality	quality	NOUN
fcis-7234	38	26	assessment	assessment	NOUN
fcis-7234	38	27	network	network	NOUN
fcis-7234	38	28	are	be	AUX
fcis-7234	38	29	improved	improve	VERB
fcis-7234	38	30	,	,	PUNCT
fcis-7234	38	31	and	and	CCONJ
fcis-7234	38	32	the	the	DET
fcis-7234	38	33	comparison	comparison	NOUN
fcis-7234	38	34	process	process	NOUN
fcis-7234	38	35	of	of	ADP
fcis-7234	38	36	the	the	DET
fcis-7234	38	37	human	human	ADJ
fcis-7234	38	38	visual	visual	ADJ
fcis-7234	38	39	system	system	NOUN
fcis-7234	38	40	can	can	AUX
fcis-7234	38	41	be	be	AUX
fcis-7234	38	42	better	well	ADV
fcis-7234	38	43	simulated	simulate	VERB
fcis-7234	38	44	at	at	ADP
fcis-7234	38	45	the	the	DET
fcis-7234	38	46	same	same	ADJ
fcis-7234	38	47	time	time	NOUN
fcis-7234	38	48	.	.	PUNCT
fcis-7234	39	1	firstly	firstly	ADV
fcis-7234	39	2	,	,	PUNCT
fcis-7234	39	3	we	we	PRON
fcis-7234	39	4	change	change	VERB
fcis-7234	39	5	the	the	DET
fcis-7234	39	6	generator	generator	NOUN
fcis-7234	39	7	structure	structure	NOUN
fcis-7234	39	8	,	,	PUNCT
fcis-7234	39	9	improve	improve	VERB
fcis-7234	39	10	the	the	DET
fcis-7234	39	11	u	u	ADJ
fcis-7234	39	12	-	-	ADJ
fcis-7234	39	13	net	net	ADJ
fcis-7234	39	14	structure	structure	NOUN
fcis-7234	39	15	,	,	PUNCT
fcis-7234	39	16	introduce	introduce	VERB
fcis-7234	39	17	the	the	DET
fcis-7234	39	18	channel	channel	NOUN
fcis-7234	39	19	attention	attention	NOUN
fcis-7234	39	20	mechanism	mechanism	NOUN
fcis-7234	39	21	senet	senet	NOUN
fcis-7234	39	22	to	to	PART
fcis-7234	39	23	let	let	VERB
fcis-7234	39	24	the	the	DET
fcis-7234	39	25	neural	neural	ADJ
fcis-7234	39	26	network	network	NOUN
fcis-7234	39	27	pay	pay	VERB
fcis-7234	39	28	more	more	ADJ
fcis-7234	39	29	attention	attention	NOUN
fcis-7234	39	30	to	to	ADP
fcis-7234	39	31	the	the	DET
fcis-7234	39	32	detailed	detailed	ADJ
fcis-7234	39	33	information	information	NOUN
fcis-7234	39	34	related	relate	VERB
fcis-7234	39	35	to	to	ADP
fcis-7234	39	36	the	the	DET
fcis-7234	39	37	features	feature	NOUN
fcis-7234	39	38	,	,	PUNCT
fcis-7234	39	39	get	get	VERB
fcis-7234	39	40	a	a	DET
fcis-7234	39	41	vector	vector	NOUN
fcis-7234	39	42	with	with	ADP
fcis-7234	39	43	the	the	DET
fcis-7234	39	44	same	same	ADJ
fcis-7234	39	45	dimension	dimension	NOUN
fcis-7234	39	46	as	as	ADP
fcis-7234	39	47	the	the	DET
fcis-7234	39	48	number	number	NOUN
fcis-7234	39	49	of	of	ADP
fcis-7234	39	50	channels	channel	NOUN
fcis-7234	39	51	,	,	PUNCT
fcis-7234	39	52	and	and	CCONJ
fcis-7234	39	53	add	add	VERB
fcis-7234	39	54	this	this	DET
fcis-7234	39	55	vector	vector	NOUN
fcis-7234	39	56	as	as	ADP
fcis-7234	39	57	a	a	DET
fcis-7234	39	58	weight	weight	NOUN
fcis-7234	39	59	number	number	NOUN
fcis-7234	39	60	to	to	ADP
fcis-7234	39	61	the	the	DET
fcis-7234	39	62	corresponding	corresponding	ADJ
fcis-7234	39	63	channels	channel	NOUN
fcis-7234	39	64	to	to	PART
fcis-7234	39	65	generate	generate	VERB
fcis-7234	39	66	more	more	ADV
fcis-7234	39	67	accurate	accurate	ADJ
fcis-7234	39	68	pseudo	pseudo	NOUN
fcis-7234	39	69	-	-	NOUN
fcis-7234	39	70	reference	reference	NOUN
fcis-7234	39	71	images	image	NOUN
fcis-7234	39	72	.	.	PUNCT
fcis-7234	40	1	secondly	secondly	ADV
fcis-7234	40	2	,	,	PUNCT
fcis-7234	40	3	a	a	DET
fcis-7234	40	4	feature	feature	NOUN
fcis-7234	40	5	similarity	similarity	NOUN
fcis-7234	40	6	measurement	measurement	NOUN
fcis-7234	40	7	system	system	NOUN
fcis-7234	40	8	is	be	AUX
fcis-7234	40	9	added	add	VERB
fcis-7234	40	10	,	,	PUNCT
fcis-7234	40	11	and	and	CCONJ
fcis-7234	40	12	a	a	DET
fcis-7234	40	13	dual	dual	ADJ
fcis-7234	40	14	discriminator	discriminator	NOUN
fcis-7234	40	15	structure	structure	NOUN
fcis-7234	40	16	is	be	AUX
fcis-7234	40	17	used	use	VERB
fcis-7234	40	18	to	to	PART
fcis-7234	40	19	discriminate	discriminate	VERB
fcis-7234	40	20	multiple	multiple	ADJ
fcis-7234	40	21	sets	set	NOUN
fcis-7234	40	22	of	of	ADP
fcis-7234	40	23	images	image	NOUN
fcis-7234	40	24	.	.	PUNCT
fcis-7234	41	1	the	the	DET
fcis-7234	41	2	fpn	fpn	PROPN
fcis-7234	41	3	structure	structure	NOUN
fcis-7234	41	4	is	be	AUX
fcis-7234	41	5	combined	combine	VERB
fcis-7234	41	6	in	in	ADP
fcis-7234	41	7	the	the	DET
fcis-7234	41	8	feature	feature	NOUN
fcis-7234	41	9	extractor	extractor	NOUN
fcis-7234	41	10	to	to	PART
fcis-7234	41	11	produce	produce	VERB
fcis-7234	41	12	a	a	DET
fcis-7234	41	13	multi	multi	ADJ
fcis-7234	41	14	-	-	ADJ
fcis-7234	41	15	scale	scale	ADJ
fcis-7234	41	16	feature	feature	NOUN
fcis-7234	41	17	representation	representation	NOUN
fcis-7234	41	18	and	and	CCONJ
fcis-7234	41	19	all	all	DET
fcis-7234	41	20	levels	level	NOUN
fcis-7234	41	21	of	of	ADP
fcis-7234	41	22	feature	feature	NOUN
fcis-7234	41	23	maps	map	NOUN
fcis-7234	41	24	with	with	ADP
fcis-7234	41	25	strong	strong	ADJ
fcis-7234	41	26	semantic	semantic	ADJ
fcis-7234	41	27	information	information	NOUN
fcis-7234	41	28	to	to	PART
fcis-7234	41	29	better	well	ADV
fcis-7234	41	30	fuse	fuse	VERB
fcis-7234	41	31	the	the	DET
fcis-7234	41	32	features	feature	NOUN
fcis-7234	41	33	of	of	ADP
fcis-7234	41	34	the	the	DET
fcis-7234	41	35	reference	reference	NOUN
fcis-7234	41	36	image	image	NOUN
fcis-7234	41	37	block	block	NOUN
fcis-7234	41	38	and	and	CCONJ
fcis-7234	41	39	the	the	DET
fcis-7234	41	40	distorted	distorted	ADJ
fcis-7234	41	41	image	image	NOUN
fcis-7234	41	42	block	block	NOUN
fcis-7234	41	43	.	.	PUNCT
fcis-7234	42	1	2	2	X
fcis-7234	42	2	.	.	NUM
fcis-7234	42	3	related	relate	VERB
fcis-7234	42	4	technologies	technology	NOUN
fcis-7234	42	5	and	and	CCONJ
fcis-7234	42	6	theories	theory	NOUN
fcis-7234	42	7	2.1	2.1	NUM
fcis-7234	42	8	.	.	PUNCT
fcis-7234	43	1	generative	generative	ADJ
fcis-7234	43	2	adversarial	adversarial	ADJ
fcis-7234	43	3	networks	network	NOUN
fcis-7234	43	4	generative	generative	VERB
fcis-7234	43	5	adversarial	adversarial	ADJ
fcis-7234	43	6	networks	network	NOUN
fcis-7234	43	7	are	be	AUX
fcis-7234	43	8	based	base	VERB
fcis-7234	43	9	on	on	ADP
fcis-7234	43	10	the	the	DET
fcis-7234	43	11	idea	idea	NOUN
fcis-7234	43	12	of	of	ADP
fcis-7234	43	13	adversarial	adversarial	ADJ
fcis-7234	43	14	training	training	NOUN
fcis-7234	43	15	in	in	ADP
fcis-7234	43	16	game	game	NOUN
fcis-7234	43	17	theory	theory	NOUN
fcis-7234	43	18	.	.	PUNCT
fcis-7234	44	1	by	by	ADP
fcis-7234	44	2	training	train	VERB
fcis-7234	44	3	two	two	NUM
fcis-7234	44	4	neural	neural	ADJ
fcis-7234	44	5	networks	network	NOUN
fcis-7234	44	6	,	,	PUNCT
fcis-7234	44	7	the	the	DET
fcis-7234	44	8	generator	generator	NOUN
fcis-7234	44	9	network	network	NOUN
fcis-7234	44	10	and	and	CCONJ
fcis-7234	44	11	the	the	DET
fcis-7234	44	12	discriminator	discriminator	NOUN
fcis-7234	44	13	network	network	NOUN
fcis-7234	44	14	,	,	PUNCT
fcis-7234	44	15	simultaneously	simultaneously	ADV
fcis-7234	44	16	,	,	PUNCT
fcis-7234	44	17	the	the	DET
fcis-7234	44	18	generator	generator	NOUN
fcis-7234	44	19	network	network	NOUN
fcis-7234	44	20	can	can	AUX
fcis-7234	44	21	generate	generate	VERB
fcis-7234	44	22	samples	sample	NOUN
fcis-7234	44	23	similar	similar	ADJ
fcis-7234	44	24	to	to	ADP
fcis-7234	44	25	the	the	DET
fcis-7234	44	26	real	real	ADJ
fcis-7234	44	27	samples	sample	NOUN
fcis-7234	44	28	,	,	PUNCT
fcis-7234	44	29	while	while	SCONJ
fcis-7234	44	30	the	the	DET
fcis-7234	44	31	discriminator	discriminator	NOUN
fcis-7234	44	32	network	network	NOUN
fcis-7234	44	33	can	can	AUX
fcis-7234	44	34	distinguish	distinguish	VERB
fcis-7234	44	35	the	the	DET
fcis-7234	44	36	real	real	ADJ
fcis-7234	44	37	samples	sample	NOUN
fcis-7234	44	38	from	from	ADP
fcis-7234	44	39	the	the	DET
fcis-7234	44	40	false	false	ADJ
fcis-7234	44	41	samples	sample	NOUN
fcis-7234	44	42	as	as	ADV
fcis-7234	44	43	accurately	accurately	ADV
fcis-7234	44	44	as	as	ADP
fcis-7234	44	45	possible	possible	ADJ
fcis-7234	44	46	.	.	PUNCT
fcis-7234	45	1	the	the	DET
fcis-7234	45	2	objective	objective	ADJ
fcis-7234	45	3	function	function	NOUN
fcis-7234	45	4	of	of	ADP
fcis-7234	45	5	the	the	DET
fcis-7234	45	6	generative	generative	ADJ
fcis-7234	45	7	adversarial	adversarial	ADJ
fcis-7234	45	8	network	network	NOUN
fcis-7234	45	9	(	(	PUNCT
fcis-7234	45	10	gan	gan	PROPN
fcis-7234	45	11	)	)	PUNCT
fcis-7234	45	12	consists	consist	VERB
fcis-7234	45	13	of	of	ADP
fcis-7234	45	14	two	two	NUM
fcis-7234	45	15	parts	part	NOUN
fcis-7234	45	16	:	:	PUNCT
fcis-7234	45	17	the	the	DET
fcis-7234	45	18	loss	loss	NOUN
fcis-7234	45	19	function	function	NOUN
fcis-7234	45	20	of	of	ADP
fcis-7234	45	21	the	the	DET
fcis-7234	45	22	generator	generator	NOUN
fcis-7234	45	23	and	and	CCONJ
fcis-7234	45	24	the	the	DET
fcis-7234	45	25	loss	loss	NOUN
fcis-7234	45	26	function	function	NOUN
fcis-7234	45	27	of	of	ADP
fcis-7234	45	28	the	the	DET
fcis-7234	45	29	discriminator	discriminator	NOUN
fcis-7234	45	30	.	.	PUNCT
fcis-7234	46	1	first	first	ADV
fcis-7234	46	2	,	,	PUNCT
fcis-7234	46	3	we	we	PRON
fcis-7234	46	4	look	look	VERB
fcis-7234	46	5	at	at	ADP
fcis-7234	46	6	the	the	DET
fcis-7234	46	7	loss	loss	NOUN
fcis-7234	46	8	function	function	NOUN
fcis-7234	46	9	of	of	ADP
fcis-7234	46	10	the	the	DET
fcis-7234	46	11	generator	generator	NOUN
fcis-7234	46	12	.	.	PUNCT
fcis-7234	47	1	the	the	DET
fcis-7234	47	2	goal	goal	NOUN
fcis-7234	47	3	of	of	ADP
fcis-7234	47	4	the	the	DET
fcis-7234	47	5	generator	generator	NOUN
fcis-7234	47	6	is	be	AUX
fcis-7234	47	7	to	to	PART
fcis-7234	47	8	generate	generate	VERB
fcis-7234	47	9	samples	sample	NOUN
fcis-7234	47	10	that	that	PRON
fcis-7234	47	11	can	can	AUX
fcis-7234	47	12	fool	fool	VERB
fcis-7234	47	13	the	the	DET
fcis-7234	47	14	discriminator	discriminator	NOUN
fcis-7234	47	15	,	,	PUNCT
fcis-7234	47	16	so	so	SCONJ
fcis-7234	47	17	the	the	DET
fcis-7234	47	18	cross	cross	NOUN
fcis-7234	47	19	-	-	NOUN
fcis-7234	47	20	entropy	entropy	NOUN
fcis-7234	47	21	can	can	AUX
fcis-7234	47	22	be	be	AUX
fcis-7234	47	23	used	use	VERB
fcis-7234	47	24	as	as	ADP
fcis-7234	47	25	the	the	DET
fcis-7234	47	26	loss	loss	NOUN
fcis-7234	47	27	function	function	NOUN
fcis-7234	47	28	of	of	ADP
fcis-7234	47	29	the	the	DET
fcis-7234	47	30	generator	generator	NOUN
fcis-7234	47	31	.	.	PUNCT
fcis-7234	48	1	its	its	PRON
fcis-7234	48	2	mathematical	mathematical	ADJ
fcis-7234	48	3	formula	formula	NOUN
fcis-7234	48	4	is	be	AUX
fcis-7234	48	5	:	:	PUNCT
fcis-7234	48	6	l_g	l_g	PUNCT
fcis-7234	48	7	=	=	PUNCT
fcis-7234	48	8	-log(d(g(z	-log(d(g(z	PROPN
fcis-7234	48	9	)	)	PUNCT
fcis-7234	48	10	)	)	PUNCT
fcis-7234	48	11	)	)	PUNCT
fcis-7234	48	12	where	where	SCONJ
fcis-7234	48	13	z	z	NOUN
fcis-7234	48	14	is	be	AUX
fcis-7234	48	15	a	a	DET
fcis-7234	48	16	vector	vector	NOUN
fcis-7234	48	17	sampled	sample	VERB
fcis-7234	48	18	from	from	ADP
fcis-7234	48	19	the	the	DET
fcis-7234	48	20	noise	noise	NOUN
fcis-7234	48	21	potential	potential	ADJ
fcis-7234	48	22	space	space	NOUN
fcis-7234	48	23	,	,	PUNCT
fcis-7234	48	24	g(z	g(z	PROPN
fcis-7234	48	25	)	)	PUNCT
fcis-7234	48	26	denotes	denote	VERB
fcis-7234	48	27	the	the	DET
fcis-7234	48	28	samples	sample	NOUN
fcis-7234	48	29	generated	generate	VERB
fcis-7234	48	30	by	by	ADP
fcis-7234	48	31	the	the	DET
fcis-7234	48	32	generator	generator	NOUN
fcis-7234	48	33	,	,	PUNCT
fcis-7234	48	34	and	and	CCONJ
fcis-7234	48	35	d(x	d(x	PROPN
fcis-7234	48	36	)	)	PUNCT
fcis-7234	48	37	is	be	AUX
fcis-7234	48	38	the	the	DET
fcis-7234	48	39	probability	probability	NOUN
fcis-7234	48	40	that	that	SCONJ
fcis-7234	48	41	the	the	DET
fcis-7234	48	42	discriminator	discriminator	NOUN
fcis-7234	48	43	output	output	NOUN
fcis-7234	48	44	sample	sample	NOUN
fcis-7234	48	45	x	x	PUNCT
fcis-7234	48	46	is	be	AUX
fcis-7234	48	47	the	the	DET
fcis-7234	48	48	true	true	ADJ
fcis-7234	48	49	sample	sample	NOUN
fcis-7234	48	50	next	next	ADV
fcis-7234	48	51	,	,	PUNCT
fcis-7234	48	52	we	we	PRON
fcis-7234	48	53	look	look	VERB
fcis-7234	48	54	at	at	ADP
fcis-7234	48	55	the	the	DET
fcis-7234	48	56	loss	loss	NOUN
fcis-7234	48	57	function	function	NOUN
fcis-7234	48	58	of	of	ADP
fcis-7234	48	59	the	the	DET
fcis-7234	48	60	discriminator	discriminator	NOUN
fcis-7234	48	61	.	.	PUNCT
fcis-7234	49	1	the	the	DET
fcis-7234	49	2	goal	goal	NOUN
fcis-7234	49	3	of	of	ADP
fcis-7234	49	4	the	the	DET
fcis-7234	49	5	discriminator	discriminator	NOUN
fcis-7234	49	6	is	be	AUX
fcis-7234	49	7	to	to	PART
fcis-7234	49	8	accurately	accurately	ADV
fcis-7234	49	9	distinguish	distinguish	VERB
fcis-7234	49	10	between	between	ADP
fcis-7234	49	11	the	the	DET
fcis-7234	49	12	true	true	ADJ
fcis-7234	49	13	samples	sample	NOUN
fcis-7234	49	14	and	and	CCONJ
fcis-7234	49	15	the	the	DET
fcis-7234	49	16	samples	sample	NOUN
fcis-7234	49	17	generated	generate	VERB
fcis-7234	49	18	by	by	ADP
fcis-7234	49	19	the	the	DET
fcis-7234	49	20	generator	generator	NOUN
fcis-7234	49	21	,	,	PUNCT
fcis-7234	49	22	so	so	ADV
fcis-7234	49	23	the	the	DET
fcis-7234	49	24	binary	binary	PROPN
fcis-7234	49	25	cross	cross	NOUN
fcis-7234	49	26	-	-	NOUN
fcis-7234	49	27	entropy	entropy	NOUN
fcis-7234	49	28	can	can	AUX
fcis-7234	49	29	be	be	AUX
fcis-7234	49	30	used	use	VERB
fcis-7234	49	31	as	as	ADP
fcis-7234	49	32	the	the	DET
fcis-7234	49	33	loss	loss	NOUN
fcis-7234	49	34	function	function	NOUN
fcis-7234	49	35	of	of	ADP
fcis-7234	49	36	the	the	DET
fcis-7234	49	37	discriminator	discriminator	NOUN
fcis-7234	49	38	.	.	PUNCT
fcis-7234	50	1	its	its	PRON
fcis-7234	50	2	mathematical	mathematical	ADJ
fcis-7234	50	3	formula	formula	NOUN
fcis-7234	50	4	is	be	AUX
fcis-7234	50	5	:	:	PUNCT
fcis-7234	50	6	l_d	l_d	PUNCT
fcis-7234	50	7	=	=	X
fcis-7234	50	8	[	[	PUNCT
fcis-7234	50	9	log(d(x	log(d(x	ADJ
fcis-7234	50	10	)	)	PUNCT
fcis-7234	50	11	)	)	PUNCT
fcis-7234	51	1	+	+	CCONJ
fcis-7234	51	2	log(1	log(1	NOUN
fcis-7234	51	3	-	-	PUNCT
fcis-7234	51	4	d(g(z	d(g(z	NOUN
fcis-7234	51	5	)	)	PUNCT
fcis-7234	51	6	)	)	PUNCT
fcis-7234	51	7	)	)	PUNCT
fcis-7234	51	8	]	]	PUNCT
fcis-7234	52	1	where	where	SCONJ
fcis-7234	52	2	x	x	PRON
fcis-7234	52	3	is	be	AUX
fcis-7234	52	4	the	the	DET
fcis-7234	52	5	true	true	ADJ
fcis-7234	52	6	sample	sample	NOUN
fcis-7234	52	7	from	from	ADP
fcis-7234	52	8	the	the	DET
fcis-7234	52	9	dataset	dataset	NOUN
fcis-7234	52	10	,	,	PUNCT
fcis-7234	52	11	d(x	d(x	PROPN
fcis-7234	52	12	)	)	PUNCT
fcis-7234	52	13	is	be	AUX
fcis-7234	52	14	the	the	DET
fcis-7234	52	15	probability	probability	NOUN
fcis-7234	52	16	that	that	SCONJ
fcis-7234	52	17	the	the	DET
fcis-7234	52	18	discriminator	discriminator	NOUN
fcis-7234	52	19	output	output	NOUN
fcis-7234	52	20	sample	sample	NOUN
fcis-7234	52	21	x	x	PUNCT
fcis-7234	52	22	is	be	AUX
fcis-7234	52	23	true	true	ADJ
fcis-7234	52	24	,	,	PUNCT
fcis-7234	52	25	g(z	g(z	ADJ
fcis-7234	52	26	)	)	PUNCT
fcis-7234	52	27	denotes	denote	VERB
fcis-7234	52	28	the	the	DET
fcis-7234	52	29	sample	sample	NOUN
fcis-7234	52	30	generated	generate	VERB
fcis-7234	52	31	by	by	ADP
fcis-7234	52	32	the	the	DET
fcis-7234	52	33	generator	generator	NOUN
fcis-7234	52	34	,	,	PUNCT
fcis-7234	52	35	and	and	CCONJ
fcis-7234	52	36	1	1	NUM
fcis-7234	52	37	-	-	PUNCT
fcis-7234	52	38	d(g(z	d(g(z	NOUN
fcis-7234	52	39	)	)	PUNCT
fcis-7234	52	40	)	)	PUNCT
fcis-7234	53	1	is	be	AUX
fcis-7234	53	2	the	the	DET
fcis-7234	53	3	probability	probability	NOUN
fcis-7234	53	4	that	that	SCONJ
fcis-7234	53	5	the	the	DET
fcis-7234	53	6	discriminator	discriminator	NOUN
fcis-7234	53	7	output	output	NOUN
fcis-7234	53	8	sample	sample	NOUN
fcis-7234	53	9	g(z	g(z	PROPN
fcis-7234	53	10	)	)	PUNCT
fcis-7234	53	11	is	be	AUX
fcis-7234	53	12	false	false	ADJ
fcis-7234	53	13	.	.	PUNCT
fcis-7234	54	1	2.2	2.2	NUM
fcis-7234	54	2	.	.	PUNCT
fcis-7234	55	1	u	u	ADJ
fcis-7234	55	2	-	-	ADJ
fcis-7234	55	3	net	net	ADJ
fcis-7234	55	4	networks	network	NOUN
fcis-7234	55	5	the	the	DET
fcis-7234	55	6	u	u	ADJ
fcis-7234	55	7	-	-	ADJ
fcis-7234	55	8	net	net	ADJ
fcis-7234	55	9	architecture	architecture	NOUN
fcis-7234	55	10	consists	consist	VERB
fcis-7234	55	11	of	of	ADP
fcis-7234	55	12	an	an	DET
fcis-7234	55	13	encoder	encoder	NOUN
fcis-7234	55	14	and	and	CCONJ
fcis-7234	55	15	a	a	DET
fcis-7234	55	16	decoder	decoder	NOUN
fcis-7234	55	17	[	[	X
fcis-7234	55	18	11	11	NUM
fcis-7234	55	19	]	]	PUNCT
fcis-7234	55	20	.	.	PUNCT
fcis-7234	56	1	the	the	DET
fcis-7234	56	2	encoder	encoder	NOUN
fcis-7234	56	3	extracts	extract	VERB
fcis-7234	56	4	high	high	ADJ
fcis-7234	56	5	-	-	PUNCT
fcis-7234	56	6	level	level	NOUN
fcis-7234	56	7	features	feature	NOUN
fcis-7234	56	8	from	from	ADP
fcis-7234	56	9	the	the	DET
fcis-7234	56	10	input	input	NOUN
fcis-7234	56	11	image	image	NOUN
fcis-7234	56	12	by	by	ADP
fcis-7234	56	13	down	down	ADV
fcis-7234	56	14	sampling	sample	VERB
fcis-7234	56	15	operations	operation	NOUN
fcis-7234	56	16	to	to	PART
fcis-7234	56	17	decompose	decompose	VERB
fcis-7234	56	18	it	it	PRON
fcis-7234	56	19	into	into	ADP
fcis-7234	56	20	smaller	small	ADJ
fcis-7234	56	21	representations	representation	NOUN
fcis-7234	56	22	.	.	PUNCT
fcis-7234	57	1	the	the	DET
fcis-7234	57	2	decoder	decoder	NOUN
fcis-7234	57	3	then	then	ADV
fcis-7234	57	4	up	up	ADP
fcis-7234	57	5	-	-	PUNCT
fcis-7234	57	6	samples	sample	NOUN
fcis-7234	57	7	by	by	ADP
fcis-7234	57	8	learning	learn	VERB
fcis-7234	57	9	filters	filter	NOUN
fcis-7234	57	10	to	to	PART
fcis-7234	57	11	reconstruct	reconstruct	VERB
fcis-7234	57	12	the	the	DET
fcis-7234	57	13	size	size	NOUN
fcis-7234	57	14	of	of	ADP
fcis-7234	57	15	the	the	DET
fcis-7234	57	16	original	original	ADJ
fcis-7234	57	17	image	image	NOUN
fcis-7234	57	18	.	.	PUNCT
fcis-7234	58	1	the	the	DET
fcis-7234	58	2	encoder	encoder	NOUN
fcis-7234	58	3	part	part	NOUN
fcis-7234	58	4	follows	follow	VERB
fcis-7234	58	5	the	the	DET
fcis-7234	58	6	typical	typical	ADJ
fcis-7234	58	7	pattern	pattern	NOUN
fcis-7234	58	8	of	of	ADP
fcis-7234	58	9	a	a	DET
fcis-7234	58	10	convolutional	convolutional	ADJ
fcis-7234	58	11	neural	neural	ADJ
fcis-7234	58	12	network	network	NOUN
fcis-7234	58	13	,	,	PUNCT
fcis-7234	58	14	where	where	SCONJ
fcis-7234	58	15	each	each	DET
fcis-7234	58	16	layer	layer	NOUN
fcis-7234	58	17	consists	consist	VERB
fcis-7234	58	18	of	of	ADP
fcis-7234	58	19	two	two	NUM
fcis-7234	58	20	3	3	NUM
fcis-7234	58	21	×	×	NOUN
fcis-7234	58	22	3	3	NUM
fcis-7234	58	23	convolutional	convolutional	ADJ
fcis-7234	58	24	layers	layer	NOUN
fcis-7234	58	25	followed	follow	VERB
fcis-7234	58	26	by	by	ADP
fcis-7234	58	27	a	a	DET
fcis-7234	58	28	batch	batch	NOUN
fcis-7234	58	29	normalization	normalization	NOUN
fcis-7234	58	30	and	and	CCONJ
fcis-7234	58	31	a	a	DET
fcis-7234	58	32	modified	modify	VERB
fcis-7234	58	33	linear	linear	NOUN
fcis-7234	58	34	unit	unit	NOUN
fcis-7234	58	35	(	(	PUNCT
fcis-7234	58	36	relu	relu	NOUN
fcis-7234	58	37	)	)	PUNCT
fcis-7234	58	38	activation	activation	NOUN
fcis-7234	58	39	function	function	NOUN
fcis-7234	58	40	,	,	PUNCT
fcis-7234	58	41	and	and	CCONJ
fcis-7234	58	42	is	be	AUX
fcis-7234	58	43	immediately	immediately	ADV
fcis-7234	58	44	followed	follow	VERB
fcis-7234	58	45	by	by	ADP
fcis-7234	58	46	a	a	DET
fcis-7234	58	47	maximum	maximum	ADJ
fcis-7234	58	48	pooling	pool	VERB
fcis-7234	58	49	operation	operation	NOUN
fcis-7234	58	50	with	with	ADP
fcis-7234	58	51	a	a	DET
fcis-7234	58	52	step	step	NOUN
fcis-7234	58	53	size	size	NOUN
fcis-7234	58	54	of	of	ADP
fcis-7234	58	55	2	2	NUM
fcis-7234	58	56	,	,	PUNCT
fcis-7234	58	57	which	which	PRON
fcis-7234	58	58	halves	halve	VERB
fcis-7234	58	59	the	the	DET
fcis-7234	58	60	resolution	resolution	NOUN
fcis-7234	58	61	of	of	ADP
fcis-7234	58	62	the	the	DET
fcis-7234	58	63	feature	feature	NOUN
fcis-7234	58	64	mapping	mapping	NOUN
fcis-7234	58	65	and	and	CCONJ
fcis-7234	58	66	reduces	reduce	VERB
fcis-7234	58	67	the	the	DET
fcis-7234	58	68	size	size	NOUN
fcis-7234	58	69	of	of	ADP
fcis-7234	58	70	the	the	DET
fcis-7234	58	71	space	space	NOUN
fcis-7234	58	72	.	.	PUNCT
fcis-7234	59	1	2.3	2.3	NUM
fcis-7234	59	2	.	.	PUNCT
fcis-7234	60	1	fsim	fsim	ADJ
fcis-7234	60	2	algorithm	algorithm	PROPN
fcis-7234	60	3	the	the	DET
fcis-7234	60	4	principle	principle	NOUN
fcis-7234	60	5	of	of	ADP
fcis-7234	60	6	fsim	fsim	ADV
fcis-7234	60	7	is	be	AUX
fcis-7234	60	8	based	base	VERB
fcis-7234	60	9	on	on	ADP
fcis-7234	60	10	the	the	DET
fcis-7234	60	11	assumption	assumption	NOUN
fcis-7234	60	12	that	that	SCONJ
fcis-7234	60	13	if	if	SCONJ
fcis-7234	60	14	two	two	NUM
fcis-7234	60	15	images	image	NOUN
fcis-7234	60	16	are	be	AUX
fcis-7234	60	17	very	very	ADV
fcis-7234	60	18	similar	similar	ADJ
fcis-7234	60	19	in	in	ADP
fcis-7234	60	20	certain	certain	ADJ
fcis-7234	60	21	features	feature	NOUN
fcis-7234	60	22	,	,	PUNCT
fcis-7234	60	23	then	then	ADV
fcis-7234	60	24	the	the	DET
fcis-7234	60	25	quality	quality	NOUN
fcis-7234	60	26	of	of	ADP
fcis-7234	60	27	these	these	DET
fcis-7234	60	28	two	two	NUM
fcis-7234	60	29	images	image	NOUN
fcis-7234	60	30	should	should	AUX
fcis-7234	60	31	be	be	AUX
fcis-7234	60	32	higher	high	ADJ
fcis-7234	60	33	.	.	PUNCT
fcis-7234	61	1	therefore	therefore	ADV
fcis-7234	61	2	,	,	PUNCT
fcis-7234	61	3	fsim	fsim	ADV
fcis-7234	61	4	first	first	ADJ
fcis-7234	61	5	calculates	calculate	VERB
fcis-7234	61	6	features	feature	NOUN
fcis-7234	61	7	such	such	ADJ
fcis-7234	61	8	as	as	ADP
fcis-7234	61	9	structural	structural	ADJ
fcis-7234	61	10	information	information	NOUN
fcis-7234	61	11	,	,	PUNCT
fcis-7234	61	12	gray	gray	ADJ
fcis-7234	61	13	value	value	NOUN
fcis-7234	61	14	and	and	CCONJ
fcis-7234	61	15	color	color	NOUN
fcis-7234	61	16	information	information	NOUN
fcis-7234	61	17	of	of	ADP
fcis-7234	61	18	an	an	DET
fcis-7234	61	19	image	image	NOUN
fcis-7234	61	20	and	and	CCONJ
fcis-7234	61	21	uses	use	VERB
fcis-7234	61	22	these	these	DET
fcis-7234	61	23	features	feature	NOUN
fcis-7234	61	24	as	as	ADP
fcis-7234	61	25	descriptors	descriptor	NOUN
fcis-7234	61	26	of	of	ADP
fcis-7234	61	27	the	the	DET
fcis-7234	61	28	image	image	NOUN
fcis-7234	61	29	.	.	PUNCT
fcis-7234	62	1	then	then	ADV
fcis-7234	62	2	,	,	PUNCT
fcis-7234	62	3	a	a	DET
fcis-7234	62	4	cosine	cosine	NOUN
fcis-7234	62	5	distance	distance	NOUN
fcis-7234	62	6	in	in	ADP
fcis-7234	62	7	one	one	NUM
fcis-7234	62	8	feature	feature	NOUN
fcis-7234	62	9	space	space	NOUN
fcis-7234	62	10	is	be	AUX
fcis-7234	62	11	used	use	VERB
fcis-7234	62	12	to	to	PART
fcis-7234	62	13	measure	measure	VERB
fcis-7234	62	14	the	the	DET
fcis-7234	62	15	similarity	similarity	NOUN
fcis-7234	62	16	between	between	ADP
fcis-7234	62	17	the	the	DET
fcis-7234	62	18	two	two	NUM
fcis-7234	62	19	images	image	NOUN
fcis-7234	62	20	.	.	PUNCT
fcis-7234	63	1	the	the	DET
fcis-7234	63	2	fsim	fsim	ADJ
fcis-7234	63	3	method	method	NOUN
fcis-7234	63	4	consists	consist	VERB
fcis-7234	63	5	of	of	ADP
fcis-7234	63	6	two	two	NUM
fcis-7234	63	7	steps	step	NOUN
fcis-7234	63	8	:	:	PUNCT
fcis-7234	63	9	feature	feature	NOUN
fcis-7234	63	10	extraction	extraction	NOUN
fcis-7234	63	11	and	and	CCONJ
fcis-7234	63	12	feature	feature	NOUN
fcis-7234	63	13	similarity	similarity	NOUN
fcis-7234	63	14	calculation	calculation	NOUN
fcis-7234	63	15	.	.	PUNCT
fcis-7234	64	1	the	the	DET
fcis-7234	64	2	feature	feature	NOUN
fcis-7234	64	3	extraction	extraction	NOUN
fcis-7234	64	4	stage	stage	NOUN
fcis-7234	64	5	applies	apply	VERB
fcis-7234	64	6	wavelet	wavelet	NOUN
fcis-7234	64	7	transforms	transform	VERB
fcis-7234	64	8	to	to	ADP
fcis-7234	64	9	the	the	DET
fcis-7234	64	10	original	original	ADJ
fcis-7234	64	11	image	image	NOUN
fcis-7234	64	12	and	and	CCONJ
fcis-7234	64	13	the	the	DET
fcis-7234	64	14	distorted	distorted	ADJ
fcis-7234	64	15	image	image	NOUN
fcis-7234	64	16	separately	separately	ADV
fcis-7234	64	17	to	to	PART
fcis-7234	64	18	extract	extract	VERB
fcis-7234	64	19	their	their	PRON
fcis-7234	64	20	energy	energy	NOUN
fcis-7234	64	21	and	and	CCONJ
fcis-7234	64	22	phase	phase	NOUN
fcis-7234	64	23	information	information	NOUN
fcis-7234	64	24	in	in	ADP
fcis-7234	64	25	different	different	ADJ
fcis-7234	64	26	frequency	frequency	NOUN
fcis-7234	64	27	subbands	subband	NOUN
fcis-7234	64	28	.	.	PUNCT
fcis-7234	65	1	the	the	DET
fcis-7234	65	2	feature	feature	NOUN
fcis-7234	65	3	similarity	similarity	NOUN
fcis-7234	65	4	calculation	calculation	NOUN
fcis-7234	65	5	stage	stage	NOUN
fcis-7234	65	6	calculates	calculate	VERB
fcis-7234	65	7	the	the	DET
fcis-7234	65	8	similarity	similarity	NOUN
fcis-7234	65	9	between	between	ADP
fcis-7234	65	10	the	the	DET
fcis-7234	65	11	two	two	NUM
fcis-7234	65	12	images	image	NOUN
fcis-7234	65	13	based	base	VERB
fcis-7234	65	14	on	on	ADP
fcis-7234	65	15	this	this	DET
fcis-7234	65	16	feature	feature	NOUN
fcis-7234	65	17	information	information	NOUN
fcis-7234	65	18	.	.	PUNCT
fcis-7234	66	1	2.4	2.4	NUM
fcis-7234	66	2	.	.	PUNCT
fcis-7234	66	3	resnet	resnet	NOUN
fcis-7234	66	4	network	network	NOUN
fcis-7234	66	5	resnet	resnet	NOUN
fcis-7234	66	6	adds	add	VERB
fcis-7234	66	7	the	the	DET
fcis-7234	66	8	output	output	NOUN
fcis-7234	66	9	of	of	ADP
fcis-7234	66	10	each	each	DET
fcis-7234	66	11	convolutional	convolutional	ADJ
fcis-7234	66	12	layer	layer	NOUN
fcis-7234	66	13	to	to	ADP
fcis-7234	66	14	the	the	DET
fcis-7234	66	15	original	original	ADJ
fcis-7234	66	16	input	input	NOUN
fcis-7234	66	17	,	,	PUNCT
fcis-7234	66	18	i.e.	i.e.	X
fcis-7234	66	19	,	,	PUNCT
fcis-7234	66	20	a	a	DET
fcis-7234	66	21	shortcut	shortcut	NOUN
fcis-7234	66	22	connection	connection	NOUN
fcis-7234	66	23	,	,	PUNCT
fcis-7234	66	24	so	so	SCONJ
fcis-7234	66	25	that	that	SCONJ
fcis-7234	66	26	the	the	DET
fcis-7234	66	27	model	model	NOUN
fcis-7234	66	28	can	can	AUX
fcis-7234	66	29	directly	directly	ADV
fcis-7234	66	30	fit	fit	VERB
fcis-7234	66	31	the	the	DET
fcis-7234	66	32	residuals	residual	NOUN
fcis-7234	66	33	and	and	CCONJ
fcis-7234	66	34	thus	thus	ADV
fcis-7234	66	35	learn	learn	VERB
fcis-7234	66	36	complex	complex	ADJ
fcis-7234	66	37	feature	feature	NOUN
fcis-7234	66	38	representations	representation	NOUN
fcis-7234	66	39	more	more	ADV
fcis-7234	66	40	easily	easily	ADV
fcis-7234	66	41	.	.	PUNCT
fcis-7234	67	1	each	each	DET
fcis-7234	67	2	residual	residual	ADJ
fcis-7234	67	3	block	block	NOUN
fcis-7234	67	4	consists	consist	VERB
fcis-7234	67	5	of	of	ADP
fcis-7234	67	6	two	two	NUM
fcis-7234	67	7	small	small	ADJ
fcis-7234	67	8	convolutional	convolutional	ADJ
fcis-7234	67	9	layers	layer	NOUN
fcis-7234	67	10	and	and	CCONJ
fcis-7234	67	11	a	a	DET
fcis-7234	67	12	cross	cross	ADJ
fcis-7234	67	13	-	-	ADJ
fcis-7234	67	14	layer	layer	ADJ
fcis-7234	67	15	connection	connection	NOUN
fcis-7234	67	16	.	.	PUNCT
fcis-7234	68	1	this	this	DET
fcis-7234	68	2	cross	cross	ADJ
fcis-7234	68	3	-	-	ADJ
fcis-7234	68	4	layer	layer	ADJ
fcis-7234	68	5	connection	connection	NOUN
fcis-7234	68	6	allows	allow	VERB
fcis-7234	68	7	the	the	DET
fcis-7234	68	8	input	input	NOUN
fcis-7234	68	9	signal	signal	NOUN
fcis-7234	68	10	to	to	PART
fcis-7234	68	11	be	be	AUX
fcis-7234	68	12	added	add	VERB
fcis-7234	68	13	directly	directly	ADV
fcis-7234	68	14	to	to	ADP
fcis-7234	68	15	the	the	DET
fcis-7234	68	16	output	output	NOUN
fcis-7234	68	17	of	of	ADP
fcis-7234	68	18	the	the	DET
fcis-7234	68	19	pooling	pooling	NOUN
fcis-7234	68	20	or	or	CCONJ
fcis-7234	68	21	convolutional	convolutional	ADJ
fcis-7234	68	22	layers	layer	NOUN
fcis-7234	68	23	without	without	ADP
fcis-7234	68	24	additional	additional	ADJ
fcis-7234	68	25	transformations	transformation	NOUN
fcis-7234	68	26	.	.	PUNCT
fcis-7234	69	1	thus	thus	ADV
fcis-7234	69	2	,	,	PUNCT
fcis-7234	69	3	if	if	SCONJ
fcis-7234	69	4	the	the	DET
fcis-7234	69	5	input	input	NOUN
fcis-7234	69	6	and	and	CCONJ
fcis-7234	69	7	output	output	NOUN
fcis-7234	69	8	of	of	ADP
fcis-7234	69	9	the	the	DET
fcis-7234	69	10	residual	residual	ADJ
fcis-7234	69	11	block	block	NOUN
fcis-7234	69	12	have	have	VERB
fcis-7234	69	13	the	the	DET
fcis-7234	69	14	same	same	ADJ
fcis-7234	69	15	dimension	dimension	NOUN
fcis-7234	69	16	,	,	PUNCT
fcis-7234	69	17	a	a	DET
fcis-7234	69	18	constant	constant	ADJ
fcis-7234	69	19	mapping	mapping	NOUN
fcis-7234	69	20	can	can	AUX
fcis-7234	69	21	be	be	AUX
fcis-7234	69	22	achieved	achieve	VERB
fcis-7234	69	23	by	by	ADP
fcis-7234	69	24	simply	simply	ADV
fcis-7234	69	25	adding	add	VERB
fcis-7234	69	26	the	the	DET
fcis-7234	69	27	input	input	NOUN
fcis-7234	69	28	to	to	ADP
fcis-7234	69	29	the	the	DET
fcis-7234	69	30	output	output	NOUN
fcis-7234	69	31	.	.	PUNCT
fcis-7234	70	1	if	if	SCONJ
fcis-7234	70	2	they	they	PRON
fcis-7234	70	3	have	have	VERB
fcis-7234	70	4	different	different	ADJ
fcis-7234	70	5	dimensions	dimension	NOUN
fcis-7234	70	6	,	,	PUNCT
fcis-7234	70	7	a	a	DET
fcis-7234	70	8	linear	linear	ADJ
fcis-7234	70	9	transformation	transformation	NOUN
fcis-7234	70	10	should	should	AUX
fcis-7234	70	11	be	be	AUX
fcis-7234	70	12	used	use	VERB
fcis-7234	70	13	to	to	PART
fcis-7234	70	14	project	project	VERB
fcis-7234	70	15	the	the	DET
fcis-7234	70	16	dimensionality	dimensionality	NOUN
fcis-7234	70	17	of	of	ADP
fcis-7234	70	18	the	the	DET
fcis-7234	70	19	input	input	NOUN
fcis-7234	70	20	in	in	ADP
fcis-7234	70	21	order	order	NOUN
fcis-7234	70	22	to	to	PART
fcis-7234	70	23	match	match	VERB
fcis-7234	70	24	the	the	DET
fcis-7234	70	25	dimensionality	dimensionality	NOUN
fcis-7234	70	26	of	of	ADP
fcis-7234	70	27	the	the	DET
fcis-7234	70	28	output	output	NOUN
fcis-7234	70	29	,	,	PUNCT
fcis-7234	70	30	and	and	CCONJ
fcis-7234	70	31	then	then	ADV
fcis-7234	70	32	add	add	VERB
fcis-7234	70	33	them	they	PRON
fcis-7234	70	34	together	together	ADV
fcis-7234	70	35	.	.	PUNCT
fcis-7234	71	1	3	3	X
fcis-7234	71	2	.	.	X
fcis-7234	71	3	no	no	DET
fcis-7234	71	4	-	-	PUNCT
fcis-7234	71	5	reference	reference	NOUN
fcis-7234	71	6	image	image	NOUN
fcis-7234	71	7	quality	quality	NOUN
fcis-7234	71	8	assessment	assessment	NOUN
fcis-7234	71	9	network	network	NOUN
fcis-7234	71	10	bon	bon	PROPN
fcis-7234	71	11	generative	generative	ADJ
fcis-7234	71	12	adversarial	adversarial	ADJ
fcis-7234	71	13	networks	network	NOUN
fcis-7234	71	14	3.1	3.1	NUM
fcis-7234	71	15	.	.	PUNCT
fcis-7234	72	1	scheme	scheme	NOUN
fcis-7234	72	2	design	design	NOUN
fcis-7234	72	3	flow	flow	NOUN
fcis-7234	72	4	the	the	DET
fcis-7234	72	5	overall	overall	ADJ
fcis-7234	72	6	flow	flow	NOUN
fcis-7234	72	7	of	of	ADP
fcis-7234	72	8	the	the	DET
fcis-7234	72	9	image	image	NOUN
fcis-7234	72	10	quality	quality	NOUN
fcis-7234	72	11	assessment	assessment	NOUN
fcis-7234	72	12	algorithm	algorithm	NOUN
fcis-7234	72	13	scheme	scheme	NOUN
fcis-7234	72	14	proposed	propose	VERB
fcis-7234	72	15	in	in	ADP
fcis-7234	72	16	this	this	DET
fcis-7234	72	17	paper	paper	NOUN
fcis-7234	72	18	is	be	AUX
fcis-7234	72	19	shown	show	VERB
fcis-7234	72	20	in	in	ADP
fcis-7234	72	21	figure	figure	NOUN
fcis-7234	72	22	2	2	NUM
fcis-7234	72	23	-	-	SYM
fcis-7234	72	24	1	1	NUM
fcis-7234	72	25	below	below	ADV
fcis-7234	72	26	:	:	PUNCT
fcis-7234	72	27	first	first	ADV
fcis-7234	72	28	,	,	PUNCT
fcis-7234	72	29	we	we	PRON
fcis-7234	72	30	take	take	VERB
fcis-7234	72	31	distorted	distort	VERB
fcis-7234	72	32	images	image	NOUN
fcis-7234	72	33	and	and	CCONJ
fcis-7234	72	34	undistorted	undistorted	ADJ
fcis-7234	72	35	original	original	ADJ
fcis-7234	72	36	images	image	NOUN
fcis-7234	72	37	in	in	ADP
fcis-7234	72	38	the	the	DET
fcis-7234	72	39	dataset	dataset	NOUN
fcis-7234	72	40	as	as	ADP
fcis-7234	72	41	inputs	input	NOUN
fcis-7234	72	42	for	for	ADP
fcis-7234	72	43	training	train	VERB
fcis-7234	72	44	the	the	DET
fcis-7234	72	45	network	network	NOUN
fcis-7234	72	46	model	model	NOUN
fcis-7234	72	47	.	.	PUNCT
fcis-7234	73	1	then	then	ADV
fcis-7234	73	2	,	,	PUNCT
fcis-7234	73	3	the	the	DET
fcis-7234	73	4	model	model	NOUN
fcis-7234	73	5	is	be	AUX
fcis-7234	73	6	used	use	VERB
fcis-7234	73	7	to	to	PART
fcis-7234	73	8	output	output	VERB
fcis-7234	73	9	the	the	DET
fcis-7234	73	10	pseudo	pseudo	NOUN
fcis-7234	73	11	-	-	NOUN
fcis-7234	73	12	reference	reference	NOUN
fcis-7234	73	13	map	map	NOUN
fcis-7234	73	14	of	of	ADP
fcis-7234	73	15	the	the	DET
fcis-7234	73	16	image	image	NOUN
fcis-7234	73	17	to	to	PART
fcis-7234	73	18	be	be	AUX
fcis-7234	73	19	tested	test	VERB
fcis-7234	73	20	,	,	PUNCT
fcis-7234	73	21	and	and	CCONJ
fcis-7234	73	22	the	the	DET
fcis-7234	73	23	depth	depth	NOUN
fcis-7234	73	24	convolution	convolution	NOUN
fcis-7234	73	25	features	feature	NOUN
fcis-7234	73	26	of	of	ADP
fcis-7234	73	27	the	the	DET
fcis-7234	73	28	pseudo	pseudo	NOUN
fcis-7234	73	29	-	-	NOUN
fcis-7234	73	30	reference	reference	NOUN
fcis-7234	73	31	map	map	NOUN
fcis-7234	73	32	are	be	AUX
fcis-7234	73	33	extracted	extract	VERB
fcis-7234	73	34	.	.	PUNCT
fcis-7234	74	1	finally	finally	ADV
fcis-7234	74	2	,	,	PUNCT
fcis-7234	74	3	the	the	DET
fcis-7234	74	4	convolutional	convolutional	ADJ
fcis-7234	74	5	features	feature	NOUN
fcis-7234	74	6	of	of	ADP
fcis-7234	74	7	the	the	DET
fcis-7234	74	8	pseudo	pseudo	NOUN
fcis-7234	74	9	-	-	NOUN
fcis-7234	74	10	reference	reference	NOUN
fcis-7234	74	11	map	map	NOUN
fcis-7234	74	12	and	and	CCONJ
fcis-7234	74	13	the	the	DET
fcis-7234	74	14	distorted	distorted	ADJ
fcis-7234	74	15	image	image	NOUN
fcis-7234	74	16	to	to	PART
fcis-7234	74	17	be	be	AUX
fcis-7234	74	18	tested	test	VERB
fcis-7234	74	19	are	be	AUX
fcis-7234	74	20	fused	fuse	VERB
fcis-7234	74	21	and	and	CCONJ
fcis-7234	74	22	fed	feed	VERB
fcis-7234	74	23	into	into	ADP
fcis-7234	74	24	the	the	DET
fcis-7234	74	25	trained	train	VERB
fcis-7234	74	26	image	image	NOUN
fcis-7234	74	27	quality	quality	NOUN
fcis-7234	74	28	assessment	assessment	NOUN
fcis-7234	74	29	regression	regression	NOUN
fcis-7234	74	30	network	network	NOUN
fcis-7234	74	31	to	to	PART
fcis-7234	74	32	obtain	obtain	VERB
fcis-7234	74	33	the	the	DET
fcis-7234	74	34	assessment	assessment	NOUN
fcis-7234	74	35	score	score	NOUN
fcis-7234	74	36	of	of	ADP
fcis-7234	74	37	theimage	theimage	NOUN
fcis-7234	74	38	.	.	PUNCT
fcis-7234	75	1	60	60	NUM
fcis-7234	75	2	fig.1	fig.1	ADJ
fcis-7234	75	3	overall	overall	ADJ
fcis-7234	75	4	flow	flow	NOUN
fcis-7234	75	5	chart	chart	NOUN
fcis-7234	75	6	of	of	ADP
fcis-7234	75	7	the	the	DET
fcis-7234	75	8	program	program	NOUN
fcis-7234	75	9	3.2	3.2	NUM
fcis-7234	75	10	.	.	PUNCT
fcis-7234	76	1	gan	gan	PROPN
fcis-7234	76	2	model	model	NOUN
fcis-7234	76	3	design	design	NOUN
fcis-7234	76	4	fig.2	fig.2	VERB
fcis-7234	76	5	architecture	architecture	NOUN
fcis-7234	76	6	diagram	diagram	NOUN
fcis-7234	76	7	of	of	ADP
fcis-7234	76	8	the	the	DET
fcis-7234	76	9	improved	improve	VERB
fcis-7234	76	10	generative	generative	ADJ
fcis-7234	76	11	adversarial	adversarial	ADJ
fcis-7234	76	12	network	network	NOUN
fcis-7234	76	13	model	model	NOUN
fcis-7234	76	14	figure	figure	NOUN
fcis-7234	76	15	2	2	NUM
fcis-7234	76	16	shows	show	VERB
fcis-7234	76	17	the	the	DET
fcis-7234	76	18	architecture	architecture	NOUN
fcis-7234	76	19	of	of	ADP
fcis-7234	76	20	the	the	DET
fcis-7234	76	21	improved	improve	VERB
fcis-7234	76	22	generator	generator	NOUN
fcis-7234	76	23	network	network	NOUN
fcis-7234	76	24	model	model	NOUN
fcis-7234	76	25	.	.	PUNCT
fcis-7234	77	1	the	the	DET
fcis-7234	77	2	pix2pix	pix2pix	ADJ
fcis-7234	77	3	structure	structure	NOUN
fcis-7234	77	4	is	be	AUX
fcis-7234	77	5	used	use	VERB
fcis-7234	77	6	as	as	ADP
fcis-7234	77	7	the	the	DET
fcis-7234	77	8	basis	basis	NOUN
fcis-7234	77	9	,	,	PUNCT
fcis-7234	77	10	which	which	PRON
fcis-7234	77	11	is	be	AUX
fcis-7234	77	12	based	base	VERB
fcis-7234	77	13	on	on	ADP
fcis-7234	77	14	the	the	DET
fcis-7234	77	15	process	process	NOUN
fcis-7234	77	16	of	of	ADP
fcis-7234	77	17	getting	get	VERB
fcis-7234	77	18	the	the	DET
fcis-7234	77	19	desired	desire	VERB
fcis-7234	77	20	output	output	NOUN
fcis-7234	77	21	image	image	NOUN
fcis-7234	77	22	from	from	ADP
fcis-7234	77	23	one	one	NUM
fcis-7234	77	24	input	input	NOUN
fcis-7234	77	25	image	image	NOUN
fcis-7234	77	26	and	and	CCONJ
fcis-7234	77	27	can	can	AUX
fcis-7234	77	28	be	be	AUX
fcis-7234	77	29	seen	see	VERB
fcis-7234	77	30	as	as	ADP
fcis-7234	77	31	a	a	DET
fcis-7234	77	32	mapping	mapping	NOUN
fcis-7234	77	33	between	between	ADP
fcis-7234	77	34	images	image	NOUN
fcis-7234	77	35	and	and	CCONJ
fcis-7234	77	36	images	image	NOUN
fcis-7234	77	37	.	.	PUNCT
fcis-7234	78	1	the	the	DET
fcis-7234	78	2	structure	structure	NOUN
fcis-7234	78	3	of	of	ADP
fcis-7234	78	4	the	the	DET
fcis-7234	78	5	generator	generator	NOUN
fcis-7234	78	6	model	model	NOUN
fcis-7234	78	7	g	g	PROPN
fcis-7234	78	8	is	be	AUX
fcis-7234	78	9	improved	improve	VERB
fcis-7234	78	10	with	with	ADP
fcis-7234	78	11	a	a	DET
fcis-7234	78	12	u	u	ADJ
fcis-7234	78	13	-	-	ADJ
fcis-7234	78	14	net	net	ADJ
fcis-7234	78	15	network	network	NOUN
fcis-7234	78	16	structure	structure	NOUN
fcis-7234	78	17	.	.	PUNCT
fcis-7234	79	1	according	accord	VERB
fcis-7234	79	2	to	to	ADP
fcis-7234	79	3	the	the	DET
fcis-7234	79	4	fsim	fsim	ADJ
fcis-7234	79	5	algorithm	algorithm	NOUN
fcis-7234	79	6	,	,	PUNCT
fcis-7234	79	7	the	the	DET
fcis-7234	79	8	feature	feature	NOUN
fcis-7234	79	9	similarity	similarity	NOUN
fcis-7234	79	10	map	map	NOUN
fcis-7234	79	11	(	(	PUNCT
fcis-7234	79	12	referred	refer	VERB
fcis-7234	79	13	to	to	ADP
fcis-7234	79	14	as	as	ADP
fcis-7234	79	15	fsim	fsim	ADJ
fcis-7234	79	16	map	map	NOUN
fcis-7234	79	17	)	)	PUNCT
fcis-7234	79	18	is	be	AUX
fcis-7234	79	19	output	output	VERB
fcis-7234	79	20	by	by	ADP
fcis-7234	79	21	learning	learn	VERB
fcis-7234	79	22	the	the	DET
fcis-7234	79	23	feature	feature	NOUN
fcis-7234	79	24	similarity	similarity	NOUN
fcis-7234	79	25	between	between	ADP
fcis-7234	79	26	the	the	DET
fcis-7234	79	27	distorted	distorted	ADJ
fcis-7234	79	28	image	image	NOUN
fcis-7234	79	29	and	and	CCONJ
fcis-7234	79	30	the	the	DET
fcis-7234	79	31	corresponding	corresponding	ADJ
fcis-7234	79	32	undistorted	undistorted	ADJ
fcis-7234	79	33	original	original	ADJ
fcis-7234	79	34	image	image	NOUN
fcis-7234	79	35	.	.	PUNCT
fcis-7234	80	1	according	accord	VERB
fcis-7234	80	2	to	to	ADP
fcis-7234	80	3	the	the	DET
fcis-7234	80	4	game	game	NOUN
fcis-7234	80	5	idea	idea	NOUN
fcis-7234	80	6	of	of	ADP
fcis-7234	80	7	generative	generative	ADJ
fcis-7234	80	8	adversarial	adversarial	ADJ
fcis-7234	80	9	network	network	NOUN
fcis-7234	80	10	,	,	PUNCT
fcis-7234	80	11	the	the	DET
fcis-7234	80	12	discriminator	discriminator	NOUN
fcis-7234	80	13	model	model	NOUN
fcis-7234	80	14	d	d	PROPN
fcis-7234	80	15	is	be	AUX
fcis-7234	80	16	needed	need	VERB
fcis-7234	80	17	to	to	PART
fcis-7234	80	18	distinguish	distinguish	VERB
fcis-7234	80	19	2	2	NUM
fcis-7234	80	20	groups	group	NOUN
fcis-7234	80	21	of	of	ADP
fcis-7234	80	22	images	image	NOUN
fcis-7234	80	23	:	:	PUNCT
fcis-7234	80	24	the	the	DET
fcis-7234	80	25	undistorted	undistorted	ADJ
fcis-7234	80	26	original	original	ADJ
fcis-7234	80	27	image	image	NOUN
fcis-7234	80	28	and	and	CCONJ
fcis-7234	80	29	the	the	DET
fcis-7234	80	30	pseudo	pseudo	NOUN
fcis-7234	80	31	-	-	NOUN
fcis-7234	80	32	reference	reference	NOUN
fcis-7234	80	33	image	image	NOUN
fcis-7234	80	34	,	,	PUNCT
fcis-7234	80	35	and	and	CCONJ
fcis-7234	80	36	the	the	DET
fcis-7234	80	37	pseudo	pseudo	NOUN
fcis-7234	80	38	-	-	ADJ
fcis-7234	80	39	fsim	fsim	ADJ
fcis-7234	80	40	map	map	NOUN
fcis-7234	80	41	and	and	CCONJ
fcis-7234	80	42	the	the	DET
fcis-7234	80	43	fsim	fsim	ADJ
fcis-7234	80	44	map	map	NOUN
fcis-7234	80	45	,	,	PUNCT
fcis-7234	80	46	so	so	SCONJ
fcis-7234	80	47	a	a	DET
fcis-7234	80	48	dual	dual	ADJ
fcis-7234	80	49	discriminator	discriminator	NOUN
fcis-7234	80	50	structure	structure	NOUN
fcis-7234	80	51	is	be	AUX
fcis-7234	80	52	used	use	VERB
fcis-7234	80	53	,	,	PUNCT
fcis-7234	80	54	and	and	CCONJ
fcis-7234	80	55	the	the	DET
fcis-7234	80	56	performance	performance	NOUN
fcis-7234	80	57	of	of	ADP
fcis-7234	80	58	the	the	DET
fcis-7234	80	59	discriminator	discriminator	NOUN
fcis-7234	80	60	will	will	AUX
fcis-7234	80	61	be	be	AUX
fcis-7234	80	62	degraded	degrade	VERB
fcis-7234	80	63	if	if	SCONJ
fcis-7234	80	64	only	only	ADV
fcis-7234	80	65	one	one	NUM
fcis-7234	80	66	discriminator	discriminator	NOUN
fcis-7234	80	67	is	be	AUX
fcis-7234	80	68	used	use	VERB
fcis-7234	80	69	to	to	PART
fcis-7234	80	70	distinguish	distinguish	VERB
fcis-7234	80	71	two	two	NUM
fcis-7234	80	72	groups	group	NOUN
fcis-7234	80	73	at	at	ADP
fcis-7234	80	74	the	the	DET
fcis-7234	80	75	same	same	ADJ
fcis-7234	80	76	time	time	NOUN
fcis-7234	80	77	.	.	PUNCT
fcis-7234	81	1	d1	d1	PROPN
fcis-7234	81	2	is	be	AUX
fcis-7234	81	3	a	a	DET
fcis-7234	81	4	convolutional	convolutional	ADJ
fcis-7234	81	5	neural	neural	ADJ
fcis-7234	81	6	network	network	NOUN
fcis-7234	81	7	(	(	PUNCT
fcis-7234	81	8	cnn	cnn	PROPN
fcis-7234	81	9	)	)	PUNCT
fcis-7234	81	10	to	to	PART
fcis-7234	81	11	distinguish	distinguish	VERB
fcis-7234	81	12	the	the	DET
fcis-7234	81	13	pseudo	pseudo	NOUN
fcis-7234	81	14	-	-	NOUN
fcis-7234	81	15	reference	reference	NOUN
fcis-7234	81	16	image	image	NOUN
fcis-7234	81	17	from	from	ADP
fcis-7234	81	18	the	the	DET
fcis-7234	81	19	original	original	ADJ
fcis-7234	81	20	image	image	NOUN
fcis-7234	81	21	to	to	PART
fcis-7234	81	22	ensure	ensure	VERB
fcis-7234	81	23	that	that	SCONJ
fcis-7234	81	24	the	the	DET
fcis-7234	81	25	generated	generate	VERB
fcis-7234	81	26	the	the	DET
fcis-7234	81	27	structure	structure	NOUN
fcis-7234	81	28	of	of	ADP
fcis-7234	81	29	d2	d2	PROPN
fcis-7234	81	30	is	be	AUX
fcis-7234	81	31	similar	similar	ADJ
fcis-7234	81	32	to	to	ADP
fcis-7234	81	33	that	that	PRON
fcis-7234	81	34	of	of	ADP
fcis-7234	81	35	d1	d1	PROPN
fcis-7234	81	36	and	and	CCONJ
fcis-7234	81	37	is	be	AUX
fcis-7234	81	38	used	use	VERB
fcis-7234	81	39	to	to	PART
fcis-7234	81	40	distinguish	distinguish	VERB
fcis-7234	81	41	the	the	DET
fcis-7234	81	42	pseudo	pseudo	NOUN
fcis-7234	81	43	fsim	fsim	ADJ
fcis-7234	81	44	image	image	NOUN
fcis-7234	81	45	from	from	ADP
fcis-7234	81	46	the	the	DET
fcis-7234	81	47	fsim	fsim	ADJ
fcis-7234	81	48	image	image	NOUN
fcis-7234	81	49	.	.	PUNCT
fcis-7234	82	1	adversarial	adversarial	ADJ
fcis-7234	82	2	training	training	NOUN
fcis-7234	82	3	using	use	VERB
fcis-7234	82	4	this	this	DET
fcis-7234	82	5	discriminator	discriminator	NOUN
fcis-7234	82	6	ensures	ensure	VERB
fcis-7234	82	7	that	that	SCONJ
fcis-7234	82	8	the	the	DET
fcis-7234	82	9	pseudo	pseudo	NOUN
fcis-7234	82	10	fsim	fsim	NOUN
fcis-7234	82	11	map	map	NOUN
fcis-7234	82	12	is	be	AUX
fcis-7234	82	13	judged	judge	VERB
fcis-7234	82	14	to	to	PART
fcis-7234	82	15	be	be	AUX
fcis-7234	82	16	as	as	ADV
fcis-7234	82	17	false	false	ADJ
fcis-7234	82	18	as	as	ADP
fcis-7234	82	19	possible	possible	ADJ
fcis-7234	82	20	and	and	CCONJ
fcis-7234	82	21	the	the	DET
fcis-7234	82	22	fsim	fsim	NOUN
fcis-7234	82	23	is	be	AUX
fcis-7234	82	24	judged	judge	VERB
fcis-7234	82	25	to	to	PART
fcis-7234	82	26	be	be	AUX
fcis-7234	82	27	as	as	ADV
fcis-7234	82	28	true	true	ADJ
fcis-7234	82	29	as	as	ADP
fcis-7234	82	30	possible	possible	ADJ
fcis-7234	82	31	.	.	PUNCT
fcis-7234	83	1	3.3	3.3	NUM
fcis-7234	83	2	.	.	PUNCT
fcis-7234	83	3	improved	improve	VERB
fcis-7234	83	4	u	u	ADJ
fcis-7234	83	5	-	-	ADJ
fcis-7234	83	6	net	net	ADJ
fcis-7234	83	7	network	network	NOUN
fcis-7234	83	8	in	in	ADP
fcis-7234	83	9	this	this	DET
fcis-7234	83	10	paper	paper	NOUN
fcis-7234	83	11	,	,	PUNCT
fcis-7234	83	12	a	a	DET
fcis-7234	83	13	new	new	ADJ
fcis-7234	83	14	generator	generator	NOUN
fcis-7234	83	15	structure	structure	NOUN
fcis-7234	83	16	is	be	AUX
fcis-7234	83	17	used	use	VERB
fcis-7234	83	18	,	,	PUNCT
fcis-7234	83	19	as	as	SCONJ
fcis-7234	83	20	shown	show	VERB
fcis-7234	83	21	in	in	ADP
fcis-7234	83	22	figure	figure	NOUN
fcis-7234	83	23	3	3	NUM
fcis-7234	83	24	.	.	PUNCT
fcis-7234	84	1	it	it	PRON
fcis-7234	84	2	utilizes	utilize	VERB
fcis-7234	84	3	a	a	DET
fcis-7234	84	4	full	full	ADJ
fcis-7234	84	5	-	-	PUNCT
fcis-7234	84	6	scale	scale	NOUN
fcis-7234	84	7	jump	jump	NOUN
fcis-7234	84	8	connection	connection	NOUN
fcis-7234	84	9	[	[	X
fcis-7234	84	10	12	12	NUM
fcis-7234	84	11	]	]	PUNCT
fcis-7234	84	12	(	(	PUNCT
fcis-7234	84	13	skip	skip	ADJ
fcis-7234	84	14	connection	connection	NOUN
fcis-7234	84	15	)	)	PUNCT
fcis-7234	84	16	and	and	CCONJ
fcis-7234	84	17	deep	deep	ADJ
fcis-7234	84	18	supervisions	supervision	NOUN
fcis-7234	84	19	[	[	X
fcis-7234	84	20	13	13	NUM
fcis-7234	84	21	]	]	PUNCT
fcis-7234	84	22	(	(	PUNCT
fcis-7234	84	23	deep	deep	ADJ
fcis-7234	84	24	supervisions	supervision	NOUN
fcis-7234	84	25	)	)	PUNCT
fcis-7234	84	26	approach	approach	NOUN
fcis-7234	84	27	.	.	PUNCT
fcis-7234	85	1	the	the	DET
fcis-7234	85	2	full	full	ADJ
fcis-7234	85	3	-	-	PUNCT
fcis-7234	85	4	scale	scale	NOUN
fcis-7234	85	5	skip	skip	NOUN
fcis-7234	85	6	connection	connection	NOUN
fcis-7234	85	7	directly	directly	ADV
fcis-7234	85	8	sums	sum	VERB
fcis-7234	85	9	the	the	DET
fcis-7234	85	10	feature	feature	NOUN
fcis-7234	85	11	maps	map	NOUN
fcis-7234	85	12	from	from	ADP
fcis-7234	85	13	different	different	ADJ
fcis-7234	85	14	levels	level	NOUN
fcis-7234	85	15	and	and	CCONJ
fcis-7234	85	16	uses	use	VERB
fcis-7234	85	17	them	they	PRON
fcis-7234	85	18	as	as	ADP
fcis-7234	85	19	input	input	NOUN
fcis-7234	85	20	for	for	ADP
fcis-7234	85	21	the	the	DET
fcis-7234	85	22	next	next	ADJ
fcis-7234	85	23	level	level	NOUN
fcis-7234	85	24	.	.	PUNCT
fcis-7234	86	1	this	this	DET
fcis-7234	86	2	jump	jump	NOUN
fcis-7234	86	3	connection	connection	NOUN
fcis-7234	86	4	is	be	AUX
fcis-7234	86	5	able	able	ADJ
fcis-7234	86	6	to	to	PART
fcis-7234	86	7	retain	retain	VERB
fcis-7234	86	8	the	the	DET
fcis-7234	86	9	detailed	detailed	ADJ
fcis-7234	86	10	information	information	NOUN
fcis-7234	86	11	of	of	ADP
fcis-7234	86	12	the	the	DET
fcis-7234	86	13	low	low	ADJ
fcis-7234	86	14	-	-	PUNCT
fcis-7234	86	15	level	level	NOUN
fcis-7234	86	16	feature	feature	NOUN
fcis-7234	86	17	maps	map	NOUN
fcis-7234	86	18	while	while	SCONJ
fcis-7234	86	19	passing	pass	VERB
fcis-7234	86	20	the	the	DET
fcis-7234	86	21	semantic	semantic	ADJ
fcis-7234	86	22	information	information	NOUN
fcis-7234	86	23	of	of	ADP
fcis-7234	86	24	the	the	DET
fcis-7234	86	25	high	high	ADJ
fcis-7234	86	26	-	-	PUNCT
fcis-7234	86	27	level	level	NOUN
fcis-7234	86	28	feature	feature	NOUN
fcis-7234	86	29	maps	map	NOUN
fcis-7234	86	30	,	,	PUNCT
fcis-7234	86	31	thus	thus	ADV
fcis-7234	86	32	effectively	effectively	ADV
fcis-7234	86	33	improving	improve	VERB
fcis-7234	86	34	the	the	DET
fcis-7234	86	35	model	model	NOUN
fcis-7234	86	36	performance	performance	NOUN
fcis-7234	86	37	.	.	PUNCT
fcis-7234	87	1	after	after	SCONJ
fcis-7234	87	2	each	each	DET
fcis-7234	87	3	decoding	decode	VERB
fcis-7234	87	4	layer	layer	NOUN
fcis-7234	87	5	generates	generate	VERB
fcis-7234	87	6	a	a	DET
fcis-7234	87	7	feature	feature	NOUN
fcis-7234	87	8	map	map	NOUN
fcis-7234	87	9	,	,	PUNCT
fcis-7234	87	10	the	the	DET
fcis-7234	87	11	deep	deep	ADJ
fcis-7234	87	12	supervision	supervision	NOUN
fcis-7234	87	13	mechanism	mechanism	NOUN
fcis-7234	87	14	feeds	feed	VERB
fcis-7234	87	15	it	it	PRON
fcis-7234	87	16	into	into	ADP
fcis-7234	87	17	a	a	DET
fcis-7234	87	18	convolutional	convolutional	ADJ
fcis-7234	87	19	layer	layer	NOUN
fcis-7234	87	20	for	for	ADP
fcis-7234	87	21	further	further	ADJ
fcis-7234	87	22	processing	processing	NOUN
fcis-7234	87	23	and	and	CCONJ
fcis-7234	87	24	performs	perform	VERB
fcis-7234	87	25	an	an	DET
fcis-7234	87	26	upsampling	upsample	VERB
fcis-7234	87	27	operation	operation	NOUN
fcis-7234	87	28	on	on	ADP
fcis-7234	87	29	the	the	DET
fcis-7234	87	30	result	result	NOUN
fcis-7234	87	31	using	use	VERB
fcis-7234	87	32	bilinear	bilinear	PROPN
fcis-7234	87	33	upsampling	upsample	VERB
fcis-7234	87	34	.	.	PUNCT
fcis-7234	88	1	the	the	DET
fcis-7234	88	2	segmentation	segmentation	NOUN
fcis-7234	88	3	result	result	NOUN
fcis-7234	88	4	obtained	obtain	VERB
fcis-7234	88	5	after	after	ADP
fcis-7234	88	6	upsampling	upsampling	NOUN
fcis-7234	88	7	is	be	AUX
fcis-7234	88	8	then	then	ADV
fcis-7234	88	9	multiplied	multiply	VERB
fcis-7234	88	10	with	with	ADP
fcis-7234	88	11	the	the	DET
fcis-7234	88	12	output	output	NOUN
fcis-7234	88	13	of	of	ADP
fcis-7234	88	14	the	the	DET
fcis-7234	88	15	classification	classification	NOUN
fcis-7234	88	16	module	module	NOUN
fcis-7234	88	17	and	and	CCONJ
fcis-7234	88	18	processed	process	VERB
fcis-7234	88	19	by	by	ADP
fcis-7234	88	20	sigmoid	sigmoid	NOUN
fcis-7234	88	21	to	to	PART
fcis-7234	88	22	obtain	obtain	VERB
fcis-7234	88	23	the	the	DET
fcis-7234	88	24	final	final	ADJ
fcis-7234	88	25	deeply	deeply	ADV
fcis-7234	88	26	supervised	supervised	ADJ
fcis-7234	88	27	output	output	NOUN
fcis-7234	88	28	.	.	PUNCT
fcis-7234	89	1	problems	problem	NOUN
fcis-7234	89	2	such	such	ADJ
fcis-7234	89	3	as	as	ADP
fcis-7234	89	4	gradient	gradient	ADJ
fcis-7234	89	5	disappearance	disappearance	NOUN
fcis-7234	89	6	and	and	CCONJ
fcis-7234	89	7	overfitting	overfitting	NOUN
fcis-7234	89	8	can	can	AUX
fcis-7234	89	9	be	be	AUX
fcis-7234	89	10	avoided	avoid	VERB
fcis-7234	89	11	,	,	PUNCT
fcis-7234	89	12	and	and	CCONJ
fcis-7234	89	13	the	the	DET
fcis-7234	89	14	generalization	generalization	NOUN
fcis-7234	89	15	ability	ability	NOUN
fcis-7234	89	16	of	of	ADP
fcis-7234	89	17	the	the	DET
fcis-7234	89	18	model	model	NOUN
fcis-7234	89	19	can	can	AUX
fcis-7234	89	20	be	be	AUX
fcis-7234	89	21	improved	improve	VERB
fcis-7234	89	22	.	.	PUNCT
fcis-7234	90	1	fig.3	fig.3	PROPN
fcis-7234	90	2	improved	improve	VERB
fcis-7234	90	3	u	u	ADJ
fcis-7234	90	4	-	-	ADJ
fcis-7234	90	5	net	net	ADJ
fcis-7234	90	6	network	network	NOUN
fcis-7234	90	7	to	to	PART
fcis-7234	90	8	improve	improve	VERB
fcis-7234	90	9	the	the	DET
fcis-7234	90	10	accuracy	accuracy	NOUN
fcis-7234	90	11	of	of	ADP
fcis-7234	90	12	the	the	DET
fcis-7234	90	13	task	task	NOUN
fcis-7234	90	14	,	,	PUNCT
fcis-7234	90	15	an	an	DET
fcis-7234	90	16	attention	attention	NOUN
fcis-7234	90	17	mechanism	mechanism	NOUN
fcis-7234	90	18	is	be	AUX
fcis-7234	90	19	added	add	VERB
fcis-7234	90	20	in	in	ADP
fcis-7234	90	21	this	this	DET
fcis-7234	90	22	paper	paper	NOUN
fcis-7234	90	23	.	.	PUNCT
fcis-7234	91	1	it	it	PRON
fcis-7234	91	2	is	be	AUX
fcis-7234	91	3	a	a	DET
fcis-7234	91	4	resource	resource	NOUN
fcis-7234	91	5	allocation	allocation	NOUN
fcis-7234	91	6	strategy	strategy	NOUN
fcis-7234	91	7	by	by	ADP
fcis-7234	91	8	attention	attention	NOUN
fcis-7234	91	9	to	to	ADP
fcis-7234	91	10	the	the	DET
fcis-7234	91	11	result	result	NOUN
fcis-7234	91	12	of	of	ADP
fcis-7234	91	13	downsampling	downsample	VERB
fcis-7234	91	14	in	in	ADP
fcis-7234	91	15	each	each	DET
fcis-7234	91	16	layer	layer	NOUN
fcis-7234	91	17	and	and	CCONJ
fcis-7234	91	18	then	then	ADV
fcis-7234	91	19	upsampling	upsample	VERB
fcis-7234	91	20	.	.	PUNCT
fcis-7234	92	1	it	it	PRON
fcis-7234	92	2	allows	allow	VERB
fcis-7234	92	3	the	the	DET
fcis-7234	92	4	neural	neural	ADJ
fcis-7234	92	5	network	network	NOUN
fcis-7234	92	6	to	to	PART
fcis-7234	92	7	pay	pay	VERB
fcis-7234	92	8	more	more	ADJ
fcis-7234	92	9	attention	attention	NOUN
fcis-7234	92	10	to	to	ADP
fcis-7234	92	11	the	the	DET
fcis-7234	92	12	detailed	detailed	ADJ
fcis-7234	92	13	information	information	NOUN
fcis-7234	92	14	related	relate	VERB
fcis-7234	92	15	to	to	ADP
fcis-7234	92	16	the	the	DET
fcis-7234	92	17	features	feature	NOUN
fcis-7234	92	18	and	and	CCONJ
fcis-7234	92	19	speeds	speed	VERB
fcis-7234	92	20	up	up	ADP
fcis-7234	92	21	the	the	DET
fcis-7234	92	22	processing	processing	NOUN
fcis-7234	92	23	time	time	NOUN
fcis-7234	92	24	of	of	ADP
fcis-7234	92	25	the	the	DET
fcis-7234	92	26	information	information	NOUN
fcis-7234	92	27	to	to	PART
fcis-7234	92	28	improve	improve	VERB
fcis-7234	92	29	the	the	DET
fcis-7234	92	30	efficiency	efficiency	NOUN
fcis-7234	92	31	of	of	ADP
fcis-7234	92	32	the	the	DET
fcis-7234	92	33	computer	computer	NOUN
fcis-7234	92	34	in	in	ADP
fcis-7234	92	35	processing	process	VERB
fcis-7234	92	36	the	the	DET
fcis-7234	92	37	information	information	NOUN
fcis-7234	92	38	.	.	PUNCT
fcis-7234	93	1	specifically	specifically	ADV
fcis-7234	93	2	,	,	PUNCT
fcis-7234	93	3	senet	senet	NOUN
fcis-7234	93	4	's	's	PART
fcis-7234	93	5	attention	attention	NOUN
fcis-7234	93	6	mechanism	mechanism	NOUN
fcis-7234	93	7	consists	consist	VERB
fcis-7234	93	8	of	of	ADP
fcis-7234	93	9	two	two	NUM
fcis-7234	93	10	steps	step	NOUN
fcis-7234	93	11	:	:	PUNCT
fcis-7234	93	12	the	the	DET
fcis-7234	93	13	squeezing	squeeze	VERB
fcis-7234	93	14	operation	operation	NOUN
fcis-7234	93	15	and	and	CCONJ
fcis-7234	93	16	the	the	DET
fcis-7234	93	17	excitation	excitation	NOUN
fcis-7234	93	18	operation	operation	NOUN
fcis-7234	93	19	.	.	PUNCT
fcis-7234	94	1	in	in	ADP
fcis-7234	94	2	the	the	DET
fcis-7234	94	3	squeezing	squeeze	VERB
fcis-7234	94	4	operation	operation	NOUN
fcis-7234	94	5	,	,	PUNCT
fcis-7234	94	6	the	the	DET
fcis-7234	94	7	feature	feature	NOUN
fcis-7234	94	8	maps	map	NOUN
fcis-7234	94	9	of	of	ADP
fcis-7234	94	10	each	each	DET
fcis-7234	94	11	channel	channel	NOUN
fcis-7234	94	12	are	be	AUX
fcis-7234	94	13	compressed	compress	VERB
fcis-7234	94	14	into	into	ADP
fcis-7234	94	15	a	a	DET
fcis-7234	94	16	vector	vector	NOUN
fcis-7234	94	17	by	by	ADP
fcis-7234	94	18	global	global	ADJ
fcis-7234	94	19	average	average	ADJ
fcis-7234	94	20	pooling	pooling	NOUN
fcis-7234	94	21	.	.	PUNCT
fcis-7234	95	1	this	this	DET
fcis-7234	95	2	vector	vector	NOUN
fcis-7234	95	3	represents	represent	VERB
fcis-7234	95	4	the	the	DET
fcis-7234	95	5	global	global	ADJ
fcis-7234	95	6	feature	feature	NOUN
fcis-7234	95	7	information	information	NOUN
fcis-7234	95	8	,	,	PUNCT
fcis-7234	95	9	where	where	SCONJ
fcis-7234	95	10	each	each	DET
fcis-7234	95	11	element	element	NOUN
fcis-7234	95	12	corresponds	correspond	VERB
fcis-7234	95	13	to	to	ADP
fcis-7234	95	14	the	the	DET
fcis-7234	95	15	average	average	NOUN
fcis-7234	95	16	of	of	ADP
fcis-7234	95	17	the	the	DET
fcis-7234	95	18	feature	feature	NOUN
fcis-7234	95	19	values	value	NOUN
fcis-7234	95	20	on	on	ADP
fcis-7234	95	21	the	the	DET
fcis-7234	95	22	corresponding	corresponding	ADJ
fcis-7234	95	23	channel	channel	NOUN
fcis-7234	95	24	.	.	PUNCT
fcis-7234	96	1	in	in	ADP
fcis-7234	96	2	this	this	DET
fcis-7234	96	3	paper	paper	NOUN
fcis-7234	96	4	,	,	PUNCT
fcis-7234	96	5	the	the	DET
fcis-7234	96	6	senet	senet	NOUN
fcis-7234	96	7	attention	attention	NOUN
fcis-7234	96	8	mechanism	mechanism	NOUN
fcis-7234	96	9	is	be	AUX
fcis-7234	96	10	chosen	choose	VERB
fcis-7234	96	11	,	,	PUNCT
fcis-7234	96	12	and	and	CCONJ
fcis-7234	96	13	the	the	DET
fcis-7234	96	14	structure	structure	NOUN
fcis-7234	96	15	is	be	AUX
fcis-7234	96	16	shown	show	VERB
fcis-7234	96	17	in	in	ADP
fcis-7234	96	18	:	:	PUNCT
fcis-7234	96	19	fig.4	fig.4	NUM
fcis-7234	96	20	attention	attention	NOUN
fcis-7234	96	21	mechanism	mechanism	NOUN
fcis-7234	96	22	module	module	NOUN
fcis-7234	96	23	3.4	3.4	NUM
fcis-7234	96	24	.	.	PUNCT
fcis-7234	96	25	image	image	NOUN
fcis-7234	96	26	quality	quality	NOUN
fcis-7234	96	27	assessment	assessment	NOUN
fcis-7234	96	28	network	network	NOUN
fcis-7234	96	29	the	the	DET
fcis-7234	96	30	iqa	iqa	PROPN
fcis-7234	96	31	network	network	NOUN
fcis-7234	96	32	consists	consist	VERB
fcis-7234	96	33	of	of	ADP
fcis-7234	96	34	three	three	NUM
fcis-7234	96	35	main	main	ADJ
fcis-7234	96	36	components	component	NOUN
fcis-7234	96	37	:	:	PUNCT
fcis-7234	96	38	a	a	DET
fcis-7234	96	39	feature	feature	NOUN
fcis-7234	96	40	extraction	extraction	NOUN
fcis-7234	96	41	network	network	NOUN
fcis-7234	96	42	,	,	PUNCT
fcis-7234	96	43	a	a	DET
fcis-7234	96	44	metric	metric	ADJ
fcis-7234	96	45	fusion	fusion	NOUN
fcis-7234	96	46	network	network	NOUN
fcis-7234	96	47	,	,	PUNCT
fcis-7234	96	48	and	and	CCONJ
fcis-7234	96	49	a	a	DET
fcis-7234	96	50	quality	quality	NOUN
fcis-7234	96	51	prediction	prediction	NOUN
fcis-7234	96	52	network	network	NOUN
fcis-7234	96	53	[	[	X
fcis-7234	96	54	14	14	NUM
fcis-7234	96	55	]	]	PUNCT
fcis-7234	96	56	.	.	PUNCT
fcis-7234	97	1	the	the	DET
fcis-7234	97	2	feature	feature	NOUN
fcis-7234	97	3	extraction	extraction	NOUN
fcis-7234	97	4	network	network	NOUN
fcis-7234	97	5	extracts	extract	NOUN
fcis-7234	97	6	features	feature	NOUN
fcis-7234	97	7	from	from	ADP
fcis-7234	97	8	the	the	DET
fcis-7234	97	9	input	input	NOUN
fcis-7234	97	10	image	image	NOUN
fcis-7234	97	11	,	,	PUNCT
fcis-7234	97	12	while	while	SCONJ
fcis-7234	97	13	the	the	DET
fcis-7234	97	14	metric	metric	ADJ
fcis-7234	97	15	fusion	fusion	NOUN
fcis-7234	97	16	network	network	NOUN
fcis-7234	97	17	combines	combine	VERB
fcis-7234	97	18	multiple	multiple	ADJ
fcis-7234	97	19	quality	quality	NOUN
fcis-7234	97	20	metrics	metric	NOUN
fcis-7234	97	21	to	to	PART
fcis-7234	97	22	generate	generate	VERB
fcis-7234	97	23	a	a	DET
fcis-7234	97	24	fused	fuse	VERB
fcis-7234	97	25	metric	metric	ADJ
fcis-7234	97	26	vector	vector	NOUN
fcis-7234	97	27	.	.	PUNCT
fcis-7234	98	1	finally	finally	ADV
fcis-7234	98	2	,	,	PUNCT
fcis-7234	98	3	the	the	DET
fcis-7234	98	4	quality	quality	NOUN
fcis-7234	98	5	prediction	prediction	NOUN
fcis-7234	98	6	network	network	NOUN
fcis-7234	98	7	predicts	predict	VERB
fcis-7234	98	8	the	the	DET
fcis-7234	98	9	final	final	ADJ
fcis-7234	98	10	quality	quality	NOUN
fcis-7234	98	11	score	score	NOUN
fcis-7234	98	12	based	base	VERB
fcis-7234	98	13	on	on	ADP
fcis-7234	98	14	the	the	DET
fcis-7234	98	15	fused	fuse	VERB
fcis-7234	98	16	metric	metric	ADJ
fcis-7234	98	17	vector	vector	NOUN
fcis-7234	98	18	.	.	PUNCT
fcis-7234	99	1	this	this	PRON
fcis-7234	99	2	is	be	AUX
fcis-7234	99	3	shown	show	VERB
fcis-7234	99	4	in	in	ADP
fcis-7234	99	5	figure	figure	NOUN
fcis-7234	99	6	5	5	NUM
fcis-7234	99	7	.	.	PUNCT
fcis-7234	100	1	in	in	ADP
fcis-7234	100	2	the	the	DET
fcis-7234	100	3	feature	feature	NOUN
fcis-7234	100	4	extraction	extraction	NOUN
fcis-7234	100	5	network	network	NOUN
fcis-7234	100	6	module	module	NOUN
fcis-7234	100	7	,	,	PUNCT
fcis-7234	100	8	two	two	NUM
fcis-7234	100	9	feature	feature	NOUN
fcis-7234	100	10	extractors	extractor	NOUN
fcis-7234	100	11	share	share	NOUN
fcis-7234	100	12	network	network	NOUN
fcis-7234	100	13	parameters	parameter	NOUN
fcis-7234	100	14	for	for	ADP
fcis-7234	100	15	extracting	extract	VERB
fcis-7234	100	16	the	the	DET
fcis-7234	100	17	input	input	NOUN
fcis-7234	100	18	pseudo	pseudo	NOUN
fcis-7234	100	19	-	-	NOUN
fcis-7234	100	20	reference	reference	NOUN
fcis-7234	100	21	image	image	NOUN
fcis-7234	100	22	block	block	NOUN
fcis-7234	100	23	and	and	CCONJ
fcis-7234	100	24	distorted	distorted	ADJ
fcis-7234	100	25	image	image	NOUN
fcis-7234	100	26	block	block	NOUN
fcis-7234	100	27	features	feature	NOUN
fcis-7234	100	28	,	,	PUNCT
fcis-7234	100	29	respectively	respectively	ADV
fcis-7234	100	30	.	.	PUNCT
fcis-7234	101	1	specifically	specifically	ADV
fcis-7234	101	2	,	,	PUNCT
fcis-7234	101	3	the	the	DET
fcis-7234	101	4	weights	weight	NOUN
fcis-7234	101	5	of	of	ADP
fcis-7234	101	6	certain	certain	ADJ
fcis-7234	101	7	convolutional	convolutional	ADJ
fcis-7234	101	8	layers	layer	NOUN
fcis-7234	101	9	are	be	AUX
fcis-7234	101	10	set	set	VERB
fcis-7234	101	11	to	to	PART
fcis-7234	101	12	be	be	AUX
fcis-7234	101	13	the	the	DET
fcis-7234	101	14	same	same	ADJ
fcis-7234	101	15	so	so	SCONJ
fcis-7234	101	16	that	that	SCONJ
fcis-7234	101	17	multiple	multiple	ADJ
fcis-7234	101	18	convolutional	convolutional	ADJ
fcis-7234	101	19	layers	layer	NOUN
fcis-7234	101	20	share	share	VERB
fcis-7234	101	21	the	the	DET
fcis-7234	101	22	same	same	ADJ
fcis-7234	101	23	set	set	NOUN
fcis-7234	101	24	of	of	ADP
fcis-7234	101	25	weights	weight	NOUN
fcis-7234	101	26	,	,	PUNCT
fcis-7234	101	27	thus	thus	ADV
fcis-7234	101	28	reducing	reduce	VERB
fcis-7234	101	29	the	the	DET
fcis-7234	101	30	number	number	NOUN
fcis-7234	101	31	of	of	ADP
fcis-7234	101	32	parameters	parameter	NOUN
fcis-7234	101	33	and	and	CCONJ
fcis-7234	101	34	improving	improve	VERB
fcis-7234	101	35	the	the	DET
fcis-7234	101	36	generalization	generalization	NOUN
fcis-7234	101	37	ability	ability	NOUN
fcis-7234	101	38	of	of	ADP
fcis-7234	101	39	the	the	DET
fcis-7234	101	40	model	model	NOUN
fcis-7234	101	41	as	as	ADV
fcis-7234	101	42	well	well	ADV
fcis-7234	101	43	as	as	ADP
fcis-7234	101	44	the	the	DET
fcis-7234	101	45	efficiency	efficiency	NOUN
fcis-7234	101	46	61	61	NUM
fcis-7234	101	47	and	and	CCONJ
fcis-7234	101	48	performance	performance	NOUN
fcis-7234	101	49	of	of	ADP
fcis-7234	101	50	the	the	DET
fcis-7234	101	51	model	model	NOUN
fcis-7234	101	52	.	.	PUNCT
fcis-7234	102	1	in	in	ADP
fcis-7234	102	2	order	order	NOUN
fcis-7234	102	3	to	to	PART
fcis-7234	102	4	solve	solve	VERB
fcis-7234	102	5	the	the	DET
fcis-7234	102	6	problem	problem	NOUN
fcis-7234	102	7	of	of	ADP
fcis-7234	102	8	distorted	distorted	ADJ
fcis-7234	102	9	images	image	NOUN
fcis-7234	102	10	with	with	ADP
fcis-7234	102	11	different	different	ADJ
fcis-7234	102	12	local	local	ADJ
fcis-7234	102	13	distortions	distortion	NOUN
fcis-7234	102	14	,	,	PUNCT
fcis-7234	102	15	a	a	DET
fcis-7234	102	16	multi	multi	ADJ
fcis-7234	102	17	-	-	ADJ
fcis-7234	102	18	scale	scale	ADJ
fcis-7234	102	19	feature	feature	NOUN
fcis-7234	102	20	extractor	extractor	NOUN
fcis-7234	102	21	scheme	scheme	NOUN
fcis-7234	102	22	is	be	AUX
fcis-7234	102	23	proposed	propose	VERB
fcis-7234	102	24	in	in	ADP
fcis-7234	102	25	this	this	DET
fcis-7234	102	26	paper	paper	NOUN
fcis-7234	102	27	.	.	PUNCT
fcis-7234	103	1	this	this	DET
fcis-7234	103	2	approach	approach	NOUN
fcis-7234	103	3	uses	use	VERB
fcis-7234	103	4	an	an	DET
fcis-7234	103	5	image	image	NOUN
fcis-7234	103	6	pyramid	pyramid	NOUN
fcis-7234	103	7	to	to	PART
fcis-7234	103	8	generate	generate	VERB
fcis-7234	103	9	images	image	NOUN
fcis-7234	103	10	at	at	ADP
fcis-7234	103	11	different	different	ADJ
fcis-7234	103	12	scales	scale	NOUN
fcis-7234	103	13	and	and	CCONJ
fcis-7234	103	14	applies	apply	VERB
fcis-7234	103	15	a	a	DET
fcis-7234	103	16	feature	feature	NOUN
fcis-7234	103	17	extractor	extractor	NOUN
fcis-7234	103	18	to	to	PART
fcis-7234	103	19	extract	extract	VERB
fcis-7234	103	20	features	feature	NOUN
fcis-7234	103	21	at	at	ADP
fcis-7234	103	22	each	each	DET
fcis-7234	103	23	level	level	NOUN
fcis-7234	103	24	.	.	PUNCT
fcis-7234	104	1	in	in	ADP
fcis-7234	104	2	this	this	DET
fcis-7234	104	3	way	way	NOUN
fcis-7234	104	4	,	,	PUNCT
fcis-7234	104	5	feature	feature	NOUN
fcis-7234	104	6	vector	vector	NOUN
fcis-7234	104	7	information	information	NOUN
fcis-7234	104	8	at	at	ADP
fcis-7234	104	9	different	different	ADJ
fcis-7234	104	10	scales	scale	NOUN
fcis-7234	104	11	can	can	AUX
fcis-7234	104	12	be	be	AUX
fcis-7234	104	13	obtained	obtain	VERB
fcis-7234	104	14	to	to	PART
fcis-7234	104	15	detect	detect	VERB
fcis-7234	104	16	overall	overall	ADJ
fcis-7234	104	17	and	and	CCONJ
fcis-7234	104	18	detailed	detailed	ADJ
fcis-7234	104	19	distortions	distortion	NOUN
fcis-7234	104	20	in	in	ADP
fcis-7234	104	21	order	order	NOUN
fcis-7234	104	22	to	to	PART
fcis-7234	104	23	improve	improve	VERB
fcis-7234	104	24	model	model	NOUN
fcis-7234	104	25	performance	performance	NOUN
fcis-7234	104	26	and	and	CCONJ
fcis-7234	104	27	robustness	robustness	NOUN
fcis-7234	104	28	.	.	PUNCT
fcis-7234	105	1	fig.5	fig.5	VERB
fcis-7234	105	2	image	image	NOUN
fcis-7234	105	3	quality	quality	NOUN
fcis-7234	105	4	assessment	assessment	NOUN
fcis-7234	105	5	network	network	NOUN
fcis-7234	105	6	in	in	ADP
fcis-7234	105	7	this	this	DET
fcis-7234	105	8	paper	paper	NOUN
fcis-7234	105	9	,	,	PUNCT
fcis-7234	105	10	the	the	DET
fcis-7234	105	11	resnet	resnet	NOUN
fcis-7234	105	12	network	network	NOUN
fcis-7234	105	13	is	be	AUX
fcis-7234	105	14	used	use	VERB
fcis-7234	105	15	as	as	ADP
fcis-7234	105	16	the	the	DET
fcis-7234	105	17	backbone	backbone	NOUN
fcis-7234	105	18	,	,	PUNCT
fcis-7234	105	19	where	where	SCONJ
fcis-7234	105	20	different	different	ADJ
fcis-7234	105	21	blocks	block	NOUN
fcis-7234	105	22	of	of	ADP
fcis-7234	105	23	output	output	NOUN
fcis-7234	105	24	feature	feature	NOUN
fcis-7234	105	25	mappings	mapping	NOUN
fcis-7234	105	26	correspond	correspond	VERB
fcis-7234	105	27	to	to	ADP
fcis-7234	105	28	different	different	ADJ
fcis-7234	105	29	semantic	semantic	ADJ
fcis-7234	105	30	information	information	NOUN
fcis-7234	105	31	.	.	PUNCT
fcis-7234	106	1	to	to	PART
fcis-7234	106	2	optimize	optimize	VERB
fcis-7234	106	3	the	the	DET
fcis-7234	106	4	alignment	alignment	NOUN
fcis-7234	106	5	of	of	ADP
fcis-7234	106	6	features	feature	NOUN
fcis-7234	106	7	with	with	ADP
fcis-7234	106	8	different	different	ADJ
fcis-7234	106	9	scales	scale	NOUN
fcis-7234	106	10	,	,	PUNCT
fcis-7234	106	11	the	the	DET
fcis-7234	106	12	feature	feature	NOUN
fcis-7234	106	13	extractor	extractor	NOUN
fcis-7234	106	14	incorporates	incorporate	VERB
fcis-7234	106	15	the	the	DET
fcis-7234	106	16	design	design	NOUN
fcis-7234	106	17	of	of	ADP
fcis-7234	106	18	feature	feature	NOUN
fcis-7234	106	19	pyramid	pyramid	NOUN
fcis-7234	106	20	networks	network	NOUN
fcis-7234	106	21	(	(	PUNCT
fcis-7234	106	22	fpn	fpn	VERB
fcis-7234	106	23	)	)	PUNCT
fcis-7234	107	1	[	[	X
fcis-7234	107	2	42	42	NUM
fcis-7234	107	3	]	]	PUNCT
fcis-7234	107	4	,	,	PUNCT
fcis-7234	107	5	where	where	SCONJ
fcis-7234	107	6	the	the	DET
fcis-7234	107	7	basic	basic	ADJ
fcis-7234	107	8	idea	idea	NOUN
fcis-7234	107	9	is	be	AUX
fcis-7234	107	10	to	to	PART
fcis-7234	107	11	construct	construct	VERB
fcis-7234	107	12	a	a	DET
fcis-7234	107	13	feature	feature	NOUN
fcis-7234	107	14	pyramid	pyramid	NOUN
fcis-7234	107	15	from	from	ADP
fcis-7234	107	16	the	the	DET
fcis-7234	107	17	input	input	NOUN
fcis-7234	107	18	image	image	NOUN
fcis-7234	107	19	,	,	PUNCT
fcis-7234	107	20	with	with	ADP
fcis-7234	107	21	high	high	ADJ
fcis-7234	107	22	-	-	PUNCT
fcis-7234	107	23	level	level	NOUN
fcis-7234	107	24	features	feature	NOUN
fcis-7234	107	25	corresponding	correspond	VERB
fcis-7234	107	26	to	to	ADP
fcis-7234	107	27	smaller	small	ADJ
fcis-7234	107	28	image	image	NOUN
fcis-7234	107	29	regions	region	NOUN
fcis-7234	107	30	.	.	PUNCT
fcis-7234	108	1	this	this	PRON
fcis-7234	108	2	allows	allow	VERB
fcis-7234	108	3	the	the	DET
fcis-7234	108	4	network	network	NOUN
fcis-7234	108	5	to	to	PART
fcis-7234	108	6	detect	detect	VERB
fcis-7234	108	7	objects	object	NOUN
fcis-7234	108	8	at	at	ADP
fcis-7234	108	9	multiple	multiple	ADJ
fcis-7234	108	10	scales	scale	NOUN
fcis-7234	108	11	and	and	CCONJ
fcis-7234	108	12	resolutions	resolution	NOUN
fcis-7234	108	13	more	more	ADV
fcis-7234	108	14	efficiently	efficiently	ADV
fcis-7234	108	15	than	than	ADP
fcis-7234	108	16	traditional	traditional	ADJ
fcis-7234	108	17	cnns	cnn	NOUN
fcis-7234	108	18	.	.	PUNCT
fcis-7234	109	1	in	in	ADP
fcis-7234	109	2	figures	figure	NOUN
fcis-7234	109	3	5	5	NUM
fcis-7234	109	4	,	,	PUNCT
fcis-7234	109	5	different	different	ADJ
fcis-7234	109	6	colors	color	NOUN
fcis-7234	109	7	indicate	indicate	VERB
fcis-7234	109	8	elemental	elemental	ADJ
fcis-7234	109	9	features	feature	NOUN
fcis-7234	109	10	with	with	ADP
fcis-7234	109	11	different	different	ADJ
fcis-7234	109	12	scales	scale	NOUN
fcis-7234	109	13	.	.	PUNCT
fcis-7234	110	1	the	the	DET
fcis-7234	110	2	resnet	resnet	NOUN
fcis-7234	110	3	network	network	NOUN
fcis-7234	110	4	is	be	AUX
fcis-7234	110	5	used	use	VERB
fcis-7234	110	6	to	to	PART
fcis-7234	110	7	extract	extract	VERB
fcis-7234	110	8	features	feature	NOUN
fcis-7234	110	9	from	from	ADP
fcis-7234	110	10	the	the	DET
fcis-7234	110	11	input	input	NOUN
fcis-7234	110	12	image	image	NOUN
fcis-7234	110	13	.	.	PUNCT
fcis-7234	111	1	the	the	DET
fcis-7234	111	2	feature	feature	NOUN
fcis-7234	111	3	pyramid	pyramid	NOUN
fcis-7234	111	4	network	network	NOUN
fcis-7234	111	5	then	then	ADV
fcis-7234	111	6	takes	take	VERB
fcis-7234	111	7	these	these	DET
fcis-7234	111	8	features	feature	NOUN
fcis-7234	111	9	and	and	CCONJ
fcis-7234	111	10	generates	generate	VERB
fcis-7234	111	11	a	a	DET
fcis-7234	111	12	pyramid	pyramid	NOUN
fcis-7234	111	13	-	-	PUNCT
fcis-7234	111	14	shaped	shape	VERB
fcis-7234	111	15	feature	feature	NOUN
fcis-7234	111	16	map	map	NOUN
fcis-7234	111	17	with	with	ADP
fcis-7234	111	18	different	different	ADJ
fcis-7234	111	19	scales	scale	NOUN
fcis-7234	111	20	.	.	PUNCT
fcis-7234	112	1	the	the	DET
fcis-7234	112	2	feature	feature	NOUN
fcis-7234	112	3	pyramid	pyramid	NOUN
fcis-7234	112	4	network	network	NOUN
fcis-7234	112	5	works	work	VERB
fcis-7234	112	6	by	by	ADP
fcis-7234	112	7	combining	combine	VERB
fcis-7234	112	8	features	feature	NOUN
fcis-7234	112	9	from	from	ADP
fcis-7234	112	10	the	the	DET
fcis-7234	112	11	earlier	early	ADJ
fcis-7234	112	12	levels	level	NOUN
fcis-7234	112	13	with	with	ADP
fcis-7234	112	14	features	feature	NOUN
fcis-7234	112	15	from	from	ADP
fcis-7234	112	16	the	the	DET
fcis-7234	112	17	later	later	ADJ
fcis-7234	112	18	levels	level	NOUN
fcis-7234	112	19	.	.	PUNCT
fcis-7234	113	1	to	to	PART
fcis-7234	113	2	finally	finally	ADV
fcis-7234	113	3	evaluate	evaluate	VERB
fcis-7234	113	4	the	the	DET
fcis-7234	113	5	quality	quality	NOUN
fcis-7234	113	6	score	score	NOUN
fcis-7234	113	7	of	of	ADP
fcis-7234	113	8	an	an	DET
fcis-7234	113	9	image	image	NOUN
fcis-7234	113	10	,	,	PUNCT
fcis-7234	113	11	we	we	PRON
fcis-7234	113	12	regress	regress	VERB
fcis-7234	113	13	the	the	DET
fcis-7234	113	14	fused	fuse	VERB
fcis-7234	113	15	features	feature	NOUN
fcis-7234	113	16	using	use	VERB
fcis-7234	113	17	two	two	NUM
fcis-7234	113	18	fully	fully	ADV
fcis-7234	113	19	connected	connected	ADJ
fcis-7234	113	20	layers	layer	NOUN
fcis-7234	113	21	.	.	PUNCT
fcis-7234	114	1	compared	compare	VERB
fcis-7234	114	2	to	to	ADP
fcis-7234	114	3	the	the	DET
fcis-7234	114	4	full	full	ADJ
fcis-7234	114	5	-	-	PUNCT
fcis-7234	114	6	reference	reference	NOUN
fcis-7234	114	7	deepiqa	deepiqa	NOUN
fcis-7234	114	8	network	network	NOUN
fcis-7234	114	9	,	,	PUNCT
fcis-7234	114	10	we	we	PRON
fcis-7234	114	11	optimize	optimize	VERB
fcis-7234	114	12	the	the	DET
fcis-7234	114	13	number	number	NOUN
fcis-7234	114	14	of	of	ADP
fcis-7234	114	15	output	output	NOUN
fcis-7234	114	16	channels	channel	NOUN
fcis-7234	114	17	of	of	ADP
fcis-7234	114	18	the	the	DET
fcis-7234	114	19	first	first	ADJ
fcis-7234	114	20	fullyconnected	fullyconnecte	VERB
fcis-7234	114	21	layer	layer	NOUN
fcis-7234	114	22	by	by	ADP
fcis-7234	114	23	reducing	reduce	VERB
fcis-7234	114	24	it	it	PRON
fcis-7234	114	25	to	to	ADP
fcis-7234	114	26	32	32	NUM
fcis-7234	114	27	,	,	PUNCT
fcis-7234	114	28	thus	thus	ADV
fcis-7234	114	29	reducing	reduce	VERB
fcis-7234	114	30	the	the	DET
fcis-7234	114	31	computational	computational	ADJ
fcis-7234	114	32	burden	burden	NOUN
fcis-7234	114	33	of	of	ADP
fcis-7234	114	34	training	train	VERB
fcis-7234	114	35	the	the	DET
fcis-7234	114	36	model	model	NOUN
fcis-7234	114	37	and	and	CCONJ
fcis-7234	114	38	also	also	ADV
fcis-7234	114	39	reducing	reduce	VERB
fcis-7234	114	40	the	the	DET
fcis-7234	114	41	number	number	NOUN
fcis-7234	114	42	of	of	ADP
fcis-7234	114	43	parameters	parameter	NOUN
fcis-7234	114	44	of	of	ADP
fcis-7234	114	45	the	the	DET
fcis-7234	114	46	algorithm	algorithm	NOUN
fcis-7234	114	47	,	,	PUNCT
fcis-7234	114	48	improving	improve	VERB
fcis-7234	114	49	the	the	DET
fcis-7234	114	50	training	training	NOUN
fcis-7234	114	51	efficiency	efficiency	NOUN
fcis-7234	114	52	of	of	ADP
fcis-7234	114	53	the	the	DET
fcis-7234	114	54	model	model	NOUN
fcis-7234	114	55	.	.	PUNCT
fcis-7234	115	1	the	the	DET
fcis-7234	115	2	scoring	scoring	NOUN
fcis-7234	115	3	regression	regression	NOUN
fcis-7234	115	4	module	module	NOUN
fcis-7234	115	5	consists	consist	VERB
fcis-7234	115	6	of	of	ADP
fcis-7234	115	7	three	three	NUM
fcis-7234	115	8	scoring	scoring	NOUN
fcis-7234	115	9	regression	regression	NOUN
fcis-7234	115	10	blocks	block	NOUN
fcis-7234	115	11	and	and	CCONJ
fcis-7234	115	12	a	a	DET
fcis-7234	115	13	weighted	weight	VERB
fcis-7234	115	14	average	average	ADJ
fcis-7234	115	15	operation	operation	NOUN
fcis-7234	115	16	to	to	PART
fcis-7234	115	17	calculate	calculate	VERB
fcis-7234	115	18	the	the	DET
fcis-7234	115	19	final	final	ADJ
fcis-7234	115	20	image	image	NOUN
fcis-7234	115	21	block	block	NOUN
fcis-7234	115	22	quality	quality	NOUN
fcis-7234	115	23	scores	score	NOUN
fcis-7234	115	24	.	.	PUNCT
fcis-7234	116	1	the	the	DET
fcis-7234	116	2	scores	score	NOUN
fcis-7234	116	3	of	of	ADP
fcis-7234	116	4	all	all	DET
fcis-7234	116	5	image	image	NOUN
fcis-7234	116	6	blocks	block	NOUN
fcis-7234	116	7	are	be	AUX
fcis-7234	116	8	aggregated	aggregate	VERB
fcis-7234	116	9	to	to	PART
fcis-7234	116	10	obtain	obtain	VERB
fcis-7234	116	11	the	the	DET
fcis-7234	116	12	quality	quality	NOUN
fcis-7234	116	13	prediction	prediction	NOUN
fcis-7234	116	14	score	score	NOUN
fcis-7234	116	15	of	of	ADP
fcis-7234	116	16	the	the	DET
fcis-7234	116	17	whole	whole	ADJ
fcis-7234	116	18	image	image	NOUN
fcis-7234	116	19	.	.	PUNCT
fcis-7234	117	1	4	4	X
fcis-7234	117	2	.	.	X
fcis-7234	117	3	experimental	experimental	ADJ
fcis-7234	117	4	results	result	NOUN
fcis-7234	117	5	4.1	4.1	NUM
fcis-7234	117	6	.	.	PUNCT
fcis-7234	118	1	data	datum	NOUN
fcis-7234	118	2	sets	set	NOUN
fcis-7234	118	3	and	and	CCONJ
fcis-7234	118	4	assessment	assessment	NOUN
fcis-7234	118	5	metrics	metric	NOUN
fcis-7234	118	6	to	to	PART
fcis-7234	118	7	verify	verify	VERB
fcis-7234	118	8	the	the	DET
fcis-7234	118	9	effectiveness	effectiveness	NOUN
fcis-7234	118	10	of	of	ADP
fcis-7234	118	11	the	the	DET
fcis-7234	118	12	model	model	NOUN
fcis-7234	118	13	proposed	propose	VERB
fcis-7234	118	14	in	in	ADP
fcis-7234	118	15	this	this	DET
fcis-7234	118	16	paper	paper	NOUN
fcis-7234	118	17	,	,	PUNCT
fcis-7234	118	18	this	this	DET
fcis-7234	118	19	experiment	experiment	NOUN
fcis-7234	118	20	was	be	AUX
fcis-7234	118	21	conducted	conduct	VERB
fcis-7234	118	22	on	on	ADP
fcis-7234	118	23	two	two	NUM
fcis-7234	118	24	real	real	ADJ
fcis-7234	118	25	distorted	distorted	ADJ
fcis-7234	118	26	image	image	NOUN
fcis-7234	118	27	datasets	dataset	NOUN
fcis-7234	118	28	,	,	PUNCT
fcis-7234	118	29	live	live	ADJ
fcis-7234	118	30	challenge	challenge	NOUN
fcis-7234	118	31	(	(	PUNCT
fcis-7234	118	32	livec	livec	PROPN
fcis-7234	118	33	)	)	PUNCT
fcis-7234	118	34	and	and	CCONJ
fcis-7234	118	35	koniq-10k	koniq-10k	PROPN
fcis-7234	118	36	,	,	PUNCT
fcis-7234	118	37	respectively	respectively	ADV
fcis-7234	118	38	.	.	PUNCT
fcis-7234	119	1	the	the	DET
fcis-7234	119	2	live	live	ADJ
fcis-7234	119	3	challenge	challenge	NOUN
fcis-7234	119	4	dataset	dataset	NOUN
fcis-7234	119	5	(	(	PUNCT
fcis-7234	119	6	(	(	PUNCT
fcis-7234	119	7	live	live	VERB
fcis-7234	119	8	in	in	ADP
fcis-7234	119	9	the	the	DET
fcis-7234	119	10	wild	wild	ADJ
fcis-7234	119	11	image	image	NOUN
fcis-7234	119	12	quality	quality	NOUN
fcis-7234	119	13	challenge	challenge	NOUN
fcis-7234	119	14	database	database	NOUN
fcis-7234	119	15	)	)	PUNCT
fcis-7234	119	16	contains	contain	VERB
fcis-7234	119	17	images	image	NOUN
fcis-7234	119	18	from	from	ADP
fcis-7234	119	19	real	real	ADJ
fcis-7234	119	20	scenes	scene	NOUN
fcis-7234	119	21	,	,	PUNCT
fcis-7234	119	22	which	which	PRON
fcis-7234	119	23	include	include	VERB
fcis-7234	119	24	various	various	ADJ
fcis-7234	119	25	environments	environment	NOUN
fcis-7234	119	26	and	and	CCONJ
fcis-7234	119	27	shooting	shooting	NOUN
fcis-7234	119	28	conditions	condition	NOUN
fcis-7234	119	29	.	.	PUNCT
fcis-7234	120	1	these	these	DET
fcis-7234	120	2	images	image	NOUN
fcis-7234	120	3	are	be	AUX
fcis-7234	120	4	used	use	VERB
fcis-7234	120	5	to	to	PART
fcis-7234	120	6	evaluate	evaluate	VERB
fcis-7234	120	7	the	the	DET
fcis-7234	120	8	performance	performance	NOUN
fcis-7234	120	9	of	of	ADP
fcis-7234	120	10	the	the	DET
fcis-7234	120	11	algorithm	algorithm	NOUN
fcis-7234	120	12	in	in	ADP
fcis-7234	120	13	real	real	ADJ
fcis-7234	120	14	scenarios	scenario	NOUN
fcis-7234	120	15	.	.	PUNCT
fcis-7234	121	1	there	there	PRON
fcis-7234	121	2	are	be	VERB
fcis-7234	121	3	1162	1162	NUM
fcis-7234	121	4	images	image	NOUN
fcis-7234	121	5	from	from	ADP
fcis-7234	121	6	real	real	ADJ
fcis-7234	121	7	scenes	scene	NOUN
fcis-7234	121	8	.	.	PUNCT
fcis-7234	122	1	the	the	DET
fcis-7234	122	2	koniq-10k	koniq-10k	PROPN
fcis-7234	122	3	dataset	dataset	NOUN
fcis-7234	122	4	is	be	AUX
fcis-7234	122	5	a	a	DET
fcis-7234	122	6	large	large	ADJ
fcis-7234	122	7	-	-	PUNCT
fcis-7234	122	8	scale	scale	NOUN
fcis-7234	122	9	dataset	dataset	NOUN
fcis-7234	122	10	for	for	ADP
fcis-7234	122	11	evaluating	evaluate	VERB
fcis-7234	122	12	image	image	NOUN
fcis-7234	122	13	quality	quality	NOUN
fcis-7234	122	14	and	and	CCONJ
fcis-7234	122	15	contains	contain	VERB
fcis-7234	122	16	10,073	10,073	NUM
fcis-7234	122	17	images	image	NOUN
fcis-7234	122	18	from	from	ADP
fcis-7234	122	19	the	the	DET
fcis-7234	122	20	internet	internet	NOUN
fcis-7234	122	21	and	and	CCONJ
fcis-7234	122	22	the	the	DET
fcis-7234	122	23	subjective	subjective	ADJ
fcis-7234	122	24	quality	quality	NOUN
fcis-7234	122	25	scores	score	NOUN
fcis-7234	122	26	associated	associate	VERB
fcis-7234	122	27	with	with	ADP
fcis-7234	122	28	them	they	PRON
fcis-7234	122	29	.	.	PUNCT
fcis-7234	123	1	developed	develop	VERB
fcis-7234	123	2	by	by	ADP
fcis-7234	123	3	the	the	DET
fcis-7234	123	4	german	german	PROPN
fcis-7234	123	5	institute	institute	NOUN
fcis-7234	123	6	for	for	ADP
fcis-7234	123	7	image	image	NOUN
fcis-7234	123	8	processing	processing	NOUN
fcis-7234	123	9	and	and	CCONJ
fcis-7234	123	10	computer	computer	NOUN
fcis-7234	123	11	vision	vision	NOUN
fcis-7234	123	12	(	(	PUNCT
fcis-7234	123	13	tnt	tnt	PROPN
fcis-7234	123	14	)	)	PUNCT
fcis-7234	123	15	,	,	PUNCT
fcis-7234	123	16	the	the	DET
fcis-7234	123	17	dataset	dataset	NOUN
fcis-7234	123	18	contains	contain	VERB
fcis-7234	123	19	images	image	NOUN
fcis-7234	123	20	from	from	ADP
fcis-7234	123	21	different	different	ADJ
fcis-7234	123	22	domains	domain	NOUN
fcis-7234	123	23	and	and	CCONJ
fcis-7234	123	24	scenes	scene	NOUN
fcis-7234	123	25	such	such	ADJ
fcis-7234	123	26	as	as	ADP
fcis-7234	123	27	natural	natural	ADJ
fcis-7234	123	28	landscapes	landscape	NOUN
fcis-7234	123	29	,	,	PUNCT
fcis-7234	123	30	human	human	ADJ
fcis-7234	123	31	portraits	portrait	NOUN
fcis-7234	123	32	,	,	PUNCT
fcis-7234	123	33	buildings	building	NOUN
fcis-7234	123	34	,	,	PUNCT
fcis-7234	123	35	animals	animal	NOUN
fcis-7234	123	36	,	,	PUNCT
fcis-7234	123	37	etc	etc	X
fcis-7234	123	38	.	.	X
fcis-7234	124	1	in	in	ADP
fcis-7234	124	2	order	order	NOUN
fcis-7234	124	3	to	to	PART
fcis-7234	124	4	compare	compare	VERB
fcis-7234	124	5	the	the	DET
fcis-7234	124	6	prediction	prediction	NOUN
fcis-7234	124	7	accuracy	accuracy	NOUN
fcis-7234	124	8	of	of	ADP
fcis-7234	124	9	the	the	DET
fcis-7234	124	10	image	image	NOUN
fcis-7234	124	11	quality	quality	NOUN
fcis-7234	124	12	assessment	assessment	NOUN
fcis-7234	124	13	methods	method	NOUN
fcis-7234	124	14	from	from	ADP
fcis-7234	124	15	an	an	DET
fcis-7234	124	16	objective	objective	ADJ
fcis-7234	124	17	point	point	NOUN
fcis-7234	124	18	of	of	ADP
fcis-7234	124	19	view	view	NOUN
fcis-7234	124	20	during	during	ADP
fcis-7234	124	21	the	the	DET
fcis-7234	124	22	testing	testing	NOUN
fcis-7234	124	23	phase	phase	NOUN
fcis-7234	124	24	,	,	PUNCT
fcis-7234	124	25	two	two	NUM
fcis-7234	124	26	assessment	assessment	NOUN
fcis-7234	124	27	metrics	metric	NOUN
fcis-7234	124	28	were	be	AUX
fcis-7234	124	29	used	use	VERB
fcis-7234	124	30	:	:	PUNCT
fcis-7234	124	31	the	the	DET
fcis-7234	124	32	pearson	pearson	PROPN
fcis-7234	124	33	linear	linear	PROPN
fcis-7234	124	34	correlation	correlation	NOUN
fcis-7234	124	35	coefficient	coefficient	NOUN
fcis-7234	124	36	(	(	PUNCT
fcis-7234	124	37	plcc	plcc	NOUN
fcis-7234	124	38	)	)	PUNCT
fcis-7234	124	39	and	and	CCONJ
fcis-7234	124	40	the	the	DET
fcis-7234	124	41	spearman	spearman	NOUN
fcis-7234	124	42	rank	rank	NOUN
fcis-7234	124	43	-	-	PUNCT
fcis-7234	124	44	order	order	NOUN
fcis-7234	124	45	correlation	correlation	NOUN
fcis-7234	124	46	coefficient	coefficient	NOUN
fcis-7234	124	47	(	(	PUNCT
fcis-7234	124	48	srocc	srocc	NOUN
fcis-7234	124	49	)	)	PUNCT
fcis-7234	124	50	correlation	correlation	NOUN
fcis-7234	124	51	coefficient	coefficient	NOUN
fcis-7234	124	52	(	(	PUNCT
fcis-7234	124	53	srocc	srocc	NOUN
fcis-7234	124	54	)	)	PUNCT
fcis-7234	124	55	.	.	PUNCT
fcis-7234	125	1	these	these	DET
fcis-7234	125	2	metrics	metric	NOUN
fcis-7234	125	3	can	can	AUX
fcis-7234	125	4	provide	provide	VERB
fcis-7234	125	5	an	an	DET
fcis-7234	125	6	objective	objective	ADJ
fcis-7234	125	7	measure	measure	NOUN
fcis-7234	125	8	of	of	ADP
fcis-7234	125	9	accuracy	accuracy	NOUN
fcis-7234	125	10	to	to	PART
fcis-7234	125	11	help	help	VERB
fcis-7234	125	12	evaluate	evaluate	VERB
fcis-7234	125	13	and	and	CCONJ
fcis-7234	125	14	compare	compare	VERB
fcis-7234	125	15	different	different	ADJ
fcis-7234	125	16	image	image	NOUN
fcis-7234	125	17	quality	quality	NOUN
fcis-7234	125	18	assessment	assessment	NOUN
fcis-7234	125	19	methods	method	NOUN
fcis-7234	125	20	.	.	PUNCT
fcis-7234	126	1	the	the	PRON
fcis-7234	126	2	closer	close	ADJ
fcis-7234	126	3	the	the	DET
fcis-7234	126	4	two	two	NUM
fcis-7234	126	5	metrics	metric	NOUN
fcis-7234	126	6	are	be	AUX
fcis-7234	126	7	to	to	ADP
fcis-7234	126	8	1	1	NUM
fcis-7234	126	9	the	the	PRON
fcis-7234	126	10	better	well	ADJ
fcis-7234	126	11	the	the	DET
fcis-7234	126	12	model	model	NOUN
fcis-7234	126	13	performance	performance	NOUN
fcis-7234	126	14	.	.	PUNCT
fcis-7234	127	1	plcc	plcc	NOUN
fcis-7234	127	2	mainly	mainly	ADV
fcis-7234	127	3	measures	measure	VERB
fcis-7234	127	4	the	the	DET
fcis-7234	127	5	linear	linear	ADJ
fcis-7234	127	6	correlation	correlation	NOUN
fcis-7234	127	7	between	between	ADP
fcis-7234	127	8	the	the	DET
fcis-7234	127	9	prediction	prediction	NOUN
fcis-7234	127	10	results	result	NOUN
fcis-7234	127	11	and	and	CCONJ
fcis-7234	127	12	the	the	DET
fcis-7234	127	13	manual	manual	ADJ
fcis-7234	127	14	subjective	subjective	ADJ
fcis-7234	127	15	assessment	assessment	NOUN
fcis-7234	127	16	results	result	VERB
fcis-7234	127	17	.	.	PUNCT
fcis-7234	128	1	the	the	DET
fcis-7234	128	2	formula	formula	NOUN
fcis-7234	128	3	is	be	AUX
fcis-7234	128	4	as	as	SCONJ
fcis-7234	128	5	follows	follow	VERB
fcis-7234	128	6	:	:	PUNCT
fcis-7234	128	7	(	(	PUNCT
fcis-7234	128	8	)	)	PUNCT
fcis-7234	128	9	(	(	PUNCT
fcis-7234	128	10	)	)	PUNCT
fcis-7234	128	11	(	(	PUNCT
fcis-7234	128	12	)	)	PUNCT
fcis-7234	128	13	(	(	PUNCT
fcis-7234	128	14	)	)	PUNCT
fcis-7234	128	15	1	1	NUM
fcis-7234	128	16	2	2	NUM
fcis-7234	128	17	2	2	NUM
fcis-7234	128	18	1	1	NUM
fcis-7234	128	19	1	1	NUM
fcis-7234	128	20	n	n	NUM
fcis-7234	129	1	i	i	PRON
fcis-7234	129	2	ii	ii	VERB
fcis-7234	129	3	n	n	CCONJ
fcis-7234	129	4	n	n	PROPN
fcis-7234	129	5	i	i	PRON
fcis-7234	129	6	ii	ii	VERB
fcis-7234	130	1	i	i	PRON
fcis-7234	130	2	s	s	VERB
fcis-7234	130	3	s	s	X
fcis-7234	130	4	p	p	X
fcis-7234	130	5	p	p	NOUN
fcis-7234	130	6	plcc	plcc	NOUN
fcis-7234	130	7	s	s	NOUN
fcis-7234	130	8	s	s	X
fcis-7234	130	9	p	p	NOUN
fcis-7234	130	10	p	p	NOUN
fcis-7234	131	1	=	=	PUNCT
fcis-7234	132	1	=	=	PUNCT
fcis-7234	133	1	=	=	PUNCT
fcis-7234	134	1	−	−	NOUN
fcis-7234	134	2	−	−	PROPN
fcis-7234	134	3	=	=	SYM
fcis-7234	135	1	−	−	PROPN
fcis-7234	135	2	−	−	NOUN
fcis-7234	135	3			X
fcis-7234	135	4			X
fcis-7234	135	5			X
fcis-7234	135	6	where	where	SCONJ
fcis-7234	135	7	n	n	PRON
fcis-7234	135	8	denotes	denote	VERB
fcis-7234	135	9	the	the	DET
fcis-7234	135	10	number	number	NOUN
fcis-7234	135	11	of	of	ADP
fcis-7234	135	12	test	test	NOUN
fcis-7234	135	13	images	image	NOUN
fcis-7234	135	14	,	,	PUNCT
fcis-7234	135	15	si	si	PROPN
fcis-7234	135	16	denotes	denote	VERB
fcis-7234	135	17	the	the	DET
fcis-7234	135	18	subjective	subjective	ADJ
fcis-7234	135	19	score	score	NOUN
fcis-7234	135	20	of	of	ADP
fcis-7234	135	21	the	the	DET
fcis-7234	135	22	ith	ith	PROPN
fcis-7234	135	23	image	image	NOUN
fcis-7234	135	24	,	,	PUNCT
fcis-7234	135	25	ip	ip	NOUN
fcis-7234	135	26	denotes	denote	NOUN
fcis-7234	135	27	the	the	DET
fcis-7234	135	28	predicted	predict	VERB
fcis-7234	135	29	score	score	NOUN
fcis-7234	135	30	of	of	ADP
fcis-7234	135	31	the	the	DET
fcis-7234	135	32	ith	ith	NOUN
fcis-7234	135	33	image	image	NOUN
fcis-7234	135	34	,	,	PUNCT
fcis-7234	135	35	and	and	CCONJ
fcis-7234	135	36	denotes	denote	VERB
fcis-7234	135	37	their	their	PRON
fcis-7234	135	38	mean	mean	NOUN
fcis-7234	135	39	.	.	PUNCT
fcis-7234	136	1	srocc	srocc	NOUN
fcis-7234	136	2	is	be	AUX
fcis-7234	136	3	a	a	DET
fcis-7234	136	4	nonparametric	nonparametric	NOUN
fcis-7234	136	5	statistic	statistic	NOUN
fcis-7234	136	6	that	that	PRON
fcis-7234	136	7	measures	measure	VERB
fcis-7234	136	8	the	the	DET
fcis-7234	136	9	monotonic	monotonic	ADJ
fcis-7234	136	10	relationship	relationship	NOUN
fcis-7234	136	11	between	between	ADP
fcis-7234	136	12	two	two	NUM
fcis-7234	136	13	variables	variable	NOUN
fcis-7234	136	14	.	.	PUNCT
fcis-7234	137	1	it	it	PRON
fcis-7234	137	2	calculates	calculate	VERB
fcis-7234	137	3	the	the	DET
fcis-7234	137	4	correlation	correlation	NOUN
fcis-7234	137	5	based	base	VERB
fcis-7234	137	6	on	on	ADP
fcis-7234	137	7	the	the	DET
fcis-7234	137	8	rank	rank	NOUN
fcis-7234	137	9	order	order	NOUN
fcis-7234	137	10	of	of	ADP
fcis-7234	137	11	the	the	DET
fcis-7234	137	12	variables	variable	NOUN
fcis-7234	137	13	rather	rather	ADV
fcis-7234	137	14	than	than	ADP
fcis-7234	137	15	the	the	DET
fcis-7234	137	16	values	value	NOUN
fcis-7234	137	17	of	of	ADP
fcis-7234	137	18	the	the	DET
fcis-7234	137	19	variables	variable	NOUN
fcis-7234	137	20	themselves	themselves	PRON
fcis-7234	137	21	.	.	PUNCT
fcis-7234	138	1	the	the	DET
fcis-7234	138	2	formula	formula	NOUN
fcis-7234	138	3	is	be	AUX
fcis-7234	138	4	as	as	SCONJ
fcis-7234	138	5	follows	follow	VERB
fcis-7234	138	6	:	:	PUNCT
fcis-7234	138	7	(	(	PUNCT
fcis-7234	138	8	)	)	PUNCT
fcis-7234	139	1	2	2	NUM
fcis-7234	139	2	1	1	NUM
fcis-7234	139	3	2	2	NUM
fcis-7234	139	4	6	6	NUM
fcis-7234	139	5	1	1	NUM
fcis-7234	139	6	1	1	NUM
fcis-7234	139	7	n	n	NUM
fcis-7234	139	8	ii	ii	NOUN
fcis-7234	139	9	d	d	X
fcis-7234	139	10	srocc	srocc	NOUN
fcis-7234	139	11	n	n	CCONJ
fcis-7234	139	12	n	n	NOUN
fcis-7234	139	13	=	=	NOUN
fcis-7234	139	14	=	=	SYM
fcis-7234	139	15	−	−	PROPN
fcis-7234	139	16	−	−	NOUN
fcis-7234	139	17			X
fcis-7234	139	18	where	where	SCONJ
fcis-7234	139	19	,	,	PUNCT
fcis-7234	139	20	2	2	NUM
fcis-7234	139	21	i	i	NOUN
fcis-7234	139	22	d	d	PROPN
fcis-7234	139	23	denotes	denote	VERB
fcis-7234	139	24	the	the	DET
fcis-7234	139	25	rank	rank	NOUN
fcis-7234	139	26	difference	difference	NOUN
fcis-7234	139	27	between	between	ADP
fcis-7234	139	28	the	the	DET
fcis-7234	139	29	subjective	subjective	ADJ
fcis-7234	139	30	and	and	CCONJ
fcis-7234	139	31	predicted	predict	VERB
fcis-7234	139	32	scores	score	NOUN
fcis-7234	139	33	of	of	ADP
fcis-7234	139	34	the	the	DET
fcis-7234	139	35	ith	ith	PROPN
fcis-7234	139	36	test	test	NOUN
fcis-7234	139	37	image	image	NOUN
fcis-7234	139	38	.	.	PUNCT
fcis-7234	140	1	4.2	4.2	NUM
fcis-7234	140	2	.	.	PUNCT
fcis-7234	140	3	parameter	parameter	NOUN
fcis-7234	140	4	setting	set	VERB
fcis-7234	140	5	the	the	DET
fcis-7234	140	6	parameters	parameter	NOUN
fcis-7234	140	7	were	be	AUX
fcis-7234	140	8	set	set	VERB
fcis-7234	140	9	to	to	PART
fcis-7234	140	10	achieve	achieve	VERB
fcis-7234	140	11	better	well	ADJ
fcis-7234	140	12	training	training	NOUN
fcis-7234	140	13	results	result	NOUN
fcis-7234	140	14	.	.	PUNCT
fcis-7234	141	1	for	for	ADP
fcis-7234	141	2	distorted	distorted	ADJ
fcis-7234	141	3	images	image	NOUN
fcis-7234	141	4	,	,	PUNCT
fcis-7234	141	5	we	we	PRON
fcis-7234	141	6	used	use	VERB
fcis-7234	141	7	a	a	DET
fcis-7234	141	8	ratio	ratio	NOUN
fcis-7234	141	9	of	of	ADP
fcis-7234	141	10	8:1:1	8:1:1	PROPN
fcis-7234	141	11	to	to	PART
fcis-7234	141	12	divide	divide	VERB
fcis-7234	141	13	the	the	DET
fcis-7234	141	14	training	training	NOUN
fcis-7234	141	15	,	,	PUNCT
fcis-7234	141	16	validation	validation	NOUN
fcis-7234	141	17	and	and	CCONJ
fcis-7234	141	18	test	test	NOUN
fcis-7234	141	19	sets	set	NOUN
fcis-7234	141	20	.	.	PUNCT
fcis-7234	142	1	for	for	ADP
fcis-7234	142	2	the	the	DET
fcis-7234	142	3	choice	choice	NOUN
fcis-7234	142	4	of	of	ADP
fcis-7234	142	5	optimizer	optimizer	NOUN
fcis-7234	142	6	,	,	PUNCT
fcis-7234	142	7	we	we	PRON
fcis-7234	142	8	used	use	VERB
fcis-7234	142	9	adam	adam	PROPN
fcis-7234	142	10	optimizer	optimizer	NOUN
fcis-7234	142	11	for	for	ADP
fcis-7234	142	12	training	training	NOUN
fcis-7234	142	13	,	,	PUNCT
fcis-7234	142	14	set	set	VERB
fcis-7234	142	15	the	the	DET
fcis-7234	142	16	initial	initial	ADJ
fcis-7234	142	17	learning	learning	NOUN
fcis-7234	142	18	rate	rate	NOUN
fcis-7234	142	19	to	to	ADP
fcis-7234	142	20	10	10	NUM
fcis-7234	142	21	-	-	SYM
fcis-7234	142	22	5	5	NUM
fcis-7234	142	23	,	,	PUNCT
fcis-7234	142	24	and	and	CCONJ
fcis-7234	142	25	set	set	VERB
fcis-7234	142	26	the	the	DET
fcis-7234	142	27	weight	weight	NOUN
fcis-7234	142	28	decay	decay	NOUN
fcis-7234	142	29	rate	rate	NOUN
fcis-7234	142	30	to	to	ADP
fcis-7234	142	31	0.0005	0.0005	NUM
fcis-7234	142	32	,	,	PUNCT
fcis-7234	142	33	a	a	DET
fcis-7234	142	34	value	value	NOUN
fcis-7234	142	35	that	that	PRON
fcis-7234	142	36	can	can	AUX
fcis-7234	142	37	try	try	VERB
fcis-7234	142	38	to	to	PART
fcis-7234	142	39	avoid	avoid	VERB
fcis-7234	142	40	overfitting	overfitte	VERB
fcis-7234	142	41	during	during	ADP
fcis-7234	142	42	the	the	DET
fcis-7234	142	43	training	training	NOUN
fcis-7234	142	44	process	process	NOUN
fcis-7234	142	45	.	.	PUNCT
fcis-7234	143	1	4.3	4.3	NUM
fcis-7234	143	2	.	.	PUNCT
fcis-7234	143	3	comparative	comparative	ADJ
fcis-7234	143	4	experiments	experiment	NOUN
fcis-7234	143	5	on	on	ADP
fcis-7234	143	6	a	a	DET
fcis-7234	143	7	single	single	ADJ
fcis-7234	143	8	data	datum	NOUN
fcis-7234	143	9	set	set	VERB
fcis-7234	143	10	in	in	ADP
fcis-7234	143	11	order	order	NOUN
fcis-7234	143	12	to	to	PART
fcis-7234	143	13	evaluate	evaluate	VERB
fcis-7234	143	14	the	the	DET
fcis-7234	143	15	effectiveness	effectiveness	NOUN
fcis-7234	143	16	of	of	ADP
fcis-7234	143	17	the	the	DET
fcis-7234	143	18	algorithms	algorithm	NOUN
fcis-7234	143	19	proposed	propose	VERB
fcis-7234	143	20	in	in	ADP
fcis-7234	143	21	this	this	DET
fcis-7234	143	22	paper	paper	NOUN
fcis-7234	143	23	,	,	PUNCT
fcis-7234	143	24	this	this	DET
fcis-7234	143	25	experiment	experiment	NOUN
fcis-7234	143	26	was	be	AUX
fcis-7234	143	27	conducted	conduct	VERB
fcis-7234	143	28	to	to	PART
fcis-7234	143	29	compare	compare	VERB
fcis-7234	143	30	with	with	ADP
fcis-7234	143	31	eight	eight	NUM
fcis-7234	143	32	higher	high	ADJ
fcis-7234	143	33	performance	performance	NOUN
fcis-7234	143	34	blind	blind	ADJ
fcis-7234	143	35	image	image	NOUN
fcis-7234	143	36	quality	quality	NOUN
fcis-7234	143	37	assessment	assessment	NOUN
fcis-7234	143	38	algorithms	algorithm	NOUN
fcis-7234	143	39	.	.	PUNCT
fcis-7234	144	1	these	these	DET
fcis-7234	144	2	nine	nine	NUM
fcis-7234	144	3	algorithms	algorithm	NOUN
fcis-7234	144	4	are	be	AUX
fcis-7234	144	5	brisque	brisque	ADJ
fcis-7234	144	6	,	,	PUNCT
fcis-7234	144	7	hosa	hosa	PROPN
fcis-7234	144	8	and	and	CCONJ
fcis-7234	144	9	cornia	cornia	PROPN
fcis-7234	144	10	,	,	PUNCT
fcis-7234	144	11	which	which	PRON
fcis-7234	144	12	are	be	AUX
fcis-7234	144	13	based	base	VERB
fcis-7234	144	14	on	on	ADP
fcis-7234	144	15	manual	manual	ADJ
fcis-7234	144	16	features	feature	NOUN
fcis-7234	144	17	,	,	PUNCT
fcis-7234	144	18	62	62	NUM
fcis-7234	144	19	and	and	CCONJ
fcis-7234	144	20	wadiqam	wadiqam	PROPN
fcis-7234	144	21	-	-	PUNCT
fcis-7234	144	22	nr	nr	PROPN
fcis-7234	144	23	,	,	PUNCT
fcis-7234	144	24	ts	ts	PROPN
fcis-7234	144	25	-	-	PUNCT
fcis-7234	144	26	cnn	cnn	PROPN
fcis-7234	144	27	,	,	PUNCT
fcis-7234	144	28	db	db	PROPN
fcis-7234	144	29	-	-	PUNCT
fcis-7234	144	30	cnn	cnn	PROPN
fcis-7234	144	31	,	,	PUNCT
fcis-7234	144	32	pqr	pqr	PROPN
fcis-7234	144	33	and	and	CCONJ
fcis-7234	144	34	hyperiqa	hyperiqa	PROPN
fcis-7234	144	35	,	,	PUNCT
fcis-7234	144	36	which	which	PRON
fcis-7234	144	37	are	be	AUX
fcis-7234	144	38	five	five	NUM
fcis-7234	144	39	classical	classical	ADJ
fcis-7234	144	40	algorithms	algorithm	NOUN
fcis-7234	144	41	based	base	VERB
fcis-7234	144	42	on	on	ADP
fcis-7234	144	43	deep	deep	ADJ
fcis-7234	144	44	learning	learning	NOUN
fcis-7234	144	45	similar	similar	ADJ
fcis-7234	144	46	to	to	ADP
fcis-7234	144	47	the	the	DET
fcis-7234	144	48	algorithms	algorithm	NOUN
fcis-7234	144	49	in	in	ADP
fcis-7234	144	50	this	this	DET
fcis-7234	144	51	paper	paper	NOUN
fcis-7234	144	52	.	.	PUNCT
fcis-7234	145	1	we	we	PRON
fcis-7234	145	2	choose	choose	VERB
fcis-7234	145	3	these	these	DET
fcis-7234	145	4	algorithms	algorithm	NOUN
fcis-7234	145	5	to	to	PART
fcis-7234	145	6	ensure	ensure	VERB
fcis-7234	145	7	the	the	DET
fcis-7234	145	8	completeness	completeness	NOUN
fcis-7234	145	9	of	of	ADP
fcis-7234	145	10	the	the	DET
fcis-7234	145	11	comparison	comparison	NOUN
fcis-7234	145	12	range	range	NOUN
fcis-7234	145	13	.	.	PUNCT
fcis-7234	146	1	first	first	ADV
fcis-7234	146	2	,	,	PUNCT
fcis-7234	146	3	this	this	DET
fcis-7234	146	4	section	section	NOUN
fcis-7234	146	5	conducts	conduct	VERB
fcis-7234	146	6	experimental	experimental	ADJ
fcis-7234	146	7	comparisons	comparison	NOUN
fcis-7234	146	8	on	on	ADP
fcis-7234	146	9	the	the	DET
fcis-7234	146	10	livec	livec	ADJ
fcis-7234	146	11	dataset	dataset	NOUN
fcis-7234	146	12	,	,	PUNCT
fcis-7234	146	13	and	and	CCONJ
fcis-7234	146	14	averages	average	NOUN
fcis-7234	146	15	are	be	AUX
fcis-7234	146	16	taken	take	VERB
fcis-7234	146	17	after	after	ADP
fcis-7234	146	18	conducting	conduct	VERB
fcis-7234	146	19	multiple	multiple	ADJ
fcis-7234	146	20	experiments	experiment	NOUN
fcis-7234	146	21	.	.	PUNCT
fcis-7234	147	1	the	the	DET
fcis-7234	147	2	experimental	experimental	ADJ
fcis-7234	147	3	results	result	NOUN
fcis-7234	147	4	are	be	AUX
fcis-7234	147	5	shown	show	VERB
fcis-7234	147	6	in	in	ADP
fcis-7234	147	7	table	table	NOUN
fcis-7234	147	8	1	1	NUM
fcis-7234	147	9	.	.	PUNCT
fcis-7234	147	10	table	table	NOUN
fcis-7234	147	11	1	1	NUM
fcis-7234	147	12	.	.	PUNCT
fcis-7234	147	13	comparison	comparison	NOUN
fcis-7234	147	14	of	of	ADP
fcis-7234	147	15	different	different	ADJ
fcis-7234	147	16	methods（livec	methods（livec	NOUN
fcis-7234	147	17	dataset	dataset	NOUN
fcis-7234	147	18	）	）	PROPN
fcis-7234	147	19	algorithm	algorithm	PROPN
fcis-7234	147	20	livec	livec	AUX
fcis-7234	147	21	srocc	srocc	VERB
fcis-7234	147	22	plcc	plcc	NOUN
fcis-7234	147	23	brisque	brisque	NOUN
fcis-7234	147	24	0.607	0.607	NUM
fcis-7234	147	25	0.630	0.630	NUM
fcis-7234	147	26	hosa	hosa	PROPN
fcis-7234	147	27	0.638	0.638	NUM
fcis-7234	147	28	0.677	0.677	NUM
fcis-7234	147	29	cornia	cornia	NOUN
fcis-7234	147	30	0.612	0.612	NUM
fcis-7234	147	31	0.653	0.653	NUM
fcis-7234	147	32	wadiqam	wadiqam	PROPN
fcis-7234	147	33	-	-	PUNCT
fcis-7234	147	34	nr	nr	PRON
fcis-7234	147	35	0.669	0.669	NUM
fcis-7234	147	36	0.679	0.679	NUM
fcis-7234	147	37	ts	t	NOUN
fcis-7234	147	38	-	-	PUNCT
fcis-7234	147	39	cnn	cnn	PROPN
fcis-7234	147	40	0.657	0.657	NUM
fcis-7234	147	41	0.668	0.668	NUM
fcis-7234	147	42	db	db	PROPN
fcis-7234	147	43	-	-	PUNCT
fcis-7234	147	44	cnn	cnn	PROPN
fcis-7234	147	45	0.848	0.848	NUM
fcis-7234	147	46	0.867	0.867	NUM
fcis-7234	147	47	pqr	pqr	PROPN
fcis-7234	147	48	0.856	0.856	NUM
fcis-7234	147	49	0.881	0.881	NUM
fcis-7234	147	50	hyperiqa	hyperiqa	NOUN
fcis-7234	147	51	0.859	0.859	NUM
fcis-7234	147	52	0.879	0.879	NUM
fcis-7234	147	53	methods	method	NOUN
fcis-7234	147	54	of	of	ADP
fcis-7234	147	55	this	this	DET
fcis-7234	147	56	paper	paper	NOUN
fcis-7234	147	57	0.867	0.867	NUM
fcis-7234	147	58	0.881	0.881	NUM
fcis-7234	147	59	as	as	SCONJ
fcis-7234	147	60	can	can	AUX
fcis-7234	147	61	be	be	AUX
fcis-7234	147	62	seen	see	VERB
fcis-7234	147	63	from	from	ADP
fcis-7234	147	64	table	table	NOUN
fcis-7234	147	65	1	1	NUM
fcis-7234	147	66	,	,	PUNCT
fcis-7234	147	67	the	the	DET
fcis-7234	147	68	srocc	srocc	NOUN
fcis-7234	147	69	as	as	ADV
fcis-7234	147	70	well	well	ADV
fcis-7234	147	71	as	as	ADP
fcis-7234	147	72	plcc	plcc	ADJ
fcis-7234	147	73	indexes	index	NOUN
fcis-7234	147	74	of	of	ADP
fcis-7234	147	75	this	this	DET
fcis-7234	147	76	paper	paper	NOUN
fcis-7234	147	77	's	's	PART
fcis-7234	147	78	method	method	NOUN
fcis-7234	147	79	outperform	outperform	VERB
fcis-7234	147	80	the	the	DET
fcis-7234	147	81	manual	manual	NOUN
fcis-7234	147	82	featurebased	featurebase	VERB
fcis-7234	147	83	methods	method	NOUN
fcis-7234	147	84	on	on	ADP
fcis-7234	147	85	the	the	DET
fcis-7234	147	86	livec	livec	ADJ
fcis-7234	147	87	real	real	ADJ
fcis-7234	147	88	distortion	distortion	NOUN
fcis-7234	147	89	image	image	NOUN
fcis-7234	147	90	dataset	dataset	NOUN
fcis-7234	147	91	,	,	PUNCT
fcis-7234	147	92	and	and	CCONJ
fcis-7234	147	93	the	the	DET
fcis-7234	147	94	improvement	improvement	NOUN
fcis-7234	147	95	effect	effect	NOUN
fcis-7234	147	96	is	be	AUX
fcis-7234	147	97	more	more	ADV
fcis-7234	147	98	obvious	obvious	ADJ
fcis-7234	147	99	,	,	PUNCT
fcis-7234	147	100	and	and	CCONJ
fcis-7234	147	101	the	the	DET
fcis-7234	147	102	same	same	ADJ
fcis-7234	147	103	better	well	ADJ
fcis-7234	147	104	performance	performance	NOUN
fcis-7234	147	105	is	be	AUX
fcis-7234	147	106	achieved	achieve	VERB
fcis-7234	147	107	compared	compare	VERB
fcis-7234	147	108	with	with	ADP
fcis-7234	147	109	the	the	DET
fcis-7234	147	110	other	other	ADJ
fcis-7234	147	111	six	six	NUM
fcis-7234	147	112	deep	deep	ADJ
fcis-7234	147	113	learning	learning	NOUN
fcis-7234	147	114	-	-	PUNCT
fcis-7234	147	115	based	base	VERB
fcis-7234	147	116	methods	method	NOUN
fcis-7234	147	117	.	.	PUNCT
fcis-7234	148	1	then	then	ADV
fcis-7234	148	2	,	,	PUNCT
fcis-7234	148	3	the	the	DET
fcis-7234	148	4	koniq-10k	koniq-10k	PROPN
fcis-7234	148	5	dataset	dataset	NOUN
fcis-7234	148	6	is	be	AUX
fcis-7234	148	7	selected	select	VERB
fcis-7234	148	8	for	for	ADP
fcis-7234	148	9	comparison	comparison	NOUN
fcis-7234	148	10	experiments	experiment	NOUN
fcis-7234	148	11	,	,	PUNCT
fcis-7234	148	12	and	and	CCONJ
fcis-7234	148	13	the	the	DET
fcis-7234	148	14	experimental	experimental	ADJ
fcis-7234	148	15	results	result	NOUN
fcis-7234	148	16	are	be	AUX
fcis-7234	148	17	shown	show	VERB
fcis-7234	148	18	in	in	ADP
fcis-7234	148	19	table	table	NOUN
fcis-7234	148	20	2	2	NUM
fcis-7234	148	21	.	.	PUNCT
fcis-7234	148	22	table	table	NOUN
fcis-7234	148	23	2	2	NUM
fcis-7234	148	24	.	.	PUNCT
fcis-7234	148	25	comparison	comparison	NOUN
fcis-7234	148	26	of	of	ADP
fcis-7234	148	27	different	different	ADJ
fcis-7234	148	28	methods（koniq-10kdataset	methods（koniq-10kdataset	PROPN
fcis-7234	148	29	）	）	PROPN
fcis-7234	148	30	algorithm	algorithm	NOUN
fcis-7234	148	31	koniq-10k	koniq-10k	PROPN
fcis-7234	148	32	srocc	srocc	VERB
fcis-7234	148	33	plcc	plcc	NOUN
fcis-7234	148	34	brisque	brisque	NOUN
fcis-7234	148	35	0.670	0.670	NUM
fcis-7234	148	36	0.683	0.683	NUM
fcis-7234	148	37	hosa	hosa	PROPN
fcis-7234	148	38	0.671	0.671	NUM
fcis-7234	148	39	0.693	0.693	NUM
fcis-7234	148	40	cornia	cornia	NOUN
fcis-7234	148	41	0.552	0.552	NUM
fcis-7234	148	42	0.570	0.570	NUM
fcis-7234	148	43	wadiqam	wadiqam	PROPN
fcis-7234	148	44	-	-	PUNCT
fcis-7234	148	45	nr	nr	PRON
fcis-7234	148	46	0.795	0.795	NUM
fcis-7234	148	47	0.806	0.806	NUM
fcis-7234	148	48	ts	ts	NOUN
fcis-7234	148	49	-	-	PUNCT
fcis-7234	148	50	cnn	cnn	PROPN
fcis-7234	148	51	0.724	0.724	NUM
fcis-7234	148	52	0.731	0.731	NUM
fcis-7234	148	53	db	db	PROPN
fcis-7234	148	54	-	-	PUNCT
fcis-7234	148	55	cnn	cnn	PROPN
fcis-7234	148	56	0.875	0.875	NUM
fcis-7234	148	57	0.883	0.883	NUM
fcis-7234	148	58	pqr	pqr	VERB
fcis-7234	148	59	0.878	0.878	NUM
fcis-7234	148	60	0.883	0.883	NUM
fcis-7234	148	61	hyperiqa	hyperiqa	NOUN
fcis-7234	148	62	0.903	0.903	NUM
fcis-7234	148	63	0.916	0.916	NUM
fcis-7234	148	64	methods	method	NOUN
fcis-7234	148	65	of	of	ADP
fcis-7234	148	66	this	this	DET
fcis-7234	148	67	paper	paper	NOUN
fcis-7234	148	68	0.905	0.905	NUM
fcis-7234	148	69	0.917	0.917	NUM
fcis-7234	148	70	as	as	SCONJ
fcis-7234	148	71	can	can	AUX
fcis-7234	148	72	be	be	AUX
fcis-7234	148	73	seen	see	VERB
fcis-7234	148	74	from	from	ADP
fcis-7234	148	75	table	table	NOUN
fcis-7234	148	76	2	2	NUM
fcis-7234	148	77	,	,	PUNCT
fcis-7234	148	78	the	the	DET
fcis-7234	148	79	method	method	NOUN
fcis-7234	148	80	in	in	ADP
fcis-7234	148	81	this	this	DET
fcis-7234	148	82	paper	paper	NOUN
fcis-7234	148	83	also	also	ADV
fcis-7234	148	84	has	have	VERB
fcis-7234	148	85	a	a	DET
fcis-7234	148	86	good	good	ADJ
fcis-7234	148	87	performance	performance	NOUN
fcis-7234	148	88	on	on	ADP
fcis-7234	148	89	the	the	DET
fcis-7234	148	90	koniq-10k	koniq-10k	PROPN
fcis-7234	148	91	dataset	dataset	VERB
fcis-7234	148	92	and	and	CCONJ
fcis-7234	148	93	achieves	achieve	VERB
fcis-7234	148	94	more	more	ADV
fcis-7234	148	95	accurate	accurate	ADJ
fcis-7234	148	96	predictions	prediction	NOUN
fcis-7234	148	97	.	.	PUNCT
fcis-7234	149	1	4.4	4.4	NUM
fcis-7234	149	2	.	.	PUNCT
fcis-7234	150	1	cross	cross	ADJ
fcis-7234	150	2	-	-	ADJ
fcis-7234	150	3	dataset	dataset	ADJ
fcis-7234	150	4	comparison	comparison	NOUN
fcis-7234	150	5	experiments	experiment	NOUN
fcis-7234	150	6	in	in	ADP
fcis-7234	150	7	order	order	NOUN
fcis-7234	150	8	to	to	PART
fcis-7234	150	9	verify	verify	VERB
fcis-7234	150	10	the	the	DET
fcis-7234	150	11	generalization	generalization	NOUN
fcis-7234	150	12	ability	ability	NOUN
fcis-7234	150	13	of	of	ADP
fcis-7234	150	14	the	the	DET
fcis-7234	150	15	algorithms	algorithm	NOUN
fcis-7234	150	16	proposed	propose	VERB
fcis-7234	150	17	in	in	ADP
fcis-7234	150	18	this	this	DET
fcis-7234	150	19	paper	paper	NOUN
fcis-7234	150	20	,	,	PUNCT
fcis-7234	150	21	comparative	comparative	ADJ
fcis-7234	150	22	experiments	experiment	NOUN
fcis-7234	150	23	are	be	AUX
fcis-7234	150	24	conducted	conduct	VERB
fcis-7234	150	25	in	in	ADP
fcis-7234	150	26	this	this	DET
fcis-7234	150	27	section	section	NOUN
fcis-7234	150	28	,	,	PUNCT
fcis-7234	150	29	and	and	CCONJ
fcis-7234	150	30	six	six	NUM
fcis-7234	150	31	advanced	advanced	ADJ
fcis-7234	150	32	algorithms	algorithm	NOUN
fcis-7234	150	33	based	base	VERB
fcis-7234	150	34	on	on	ADP
fcis-7234	150	35	deep	deep	ADJ
fcis-7234	150	36	learning	learning	NOUN
fcis-7234	150	37	are	be	AUX
fcis-7234	150	38	selected	select	VERB
fcis-7234	150	39	to	to	PART
fcis-7234	150	40	evaluate	evaluate	VERB
fcis-7234	150	41	the	the	DET
fcis-7234	150	42	generalization	generalization	NOUN
fcis-7234	150	43	ability	ability	NOUN
fcis-7234	150	44	of	of	ADP
fcis-7234	150	45	the	the	DET
fcis-7234	150	46	models	model	NOUN
fcis-7234	150	47	.	.	PUNCT
fcis-7234	151	1	the	the	DET
fcis-7234	151	2	experimental	experimental	ADJ
fcis-7234	151	3	results	result	NOUN
fcis-7234	151	4	are	be	AUX
fcis-7234	151	5	detailed	detail	VERB
fcis-7234	151	6	in	in	ADP
fcis-7234	151	7	table	table	NOUN
fcis-7234	151	8	3	3	NUM
fcis-7234	151	9	.	.	PUNCT
fcis-7234	151	10	table	table	NOUN
fcis-7234	151	11	3	3	NUM
fcis-7234	151	12	.	.	PUNCT
fcis-7234	151	13	srocc	srocc	NOUN
fcis-7234	151	14	for	for	ADP
fcis-7234	151	15	cross	cross	ADJ
fcis-7234	151	16	-	-	ADJ
fcis-7234	151	17	dataset	dataset	ADJ
fcis-7234	151	18	testing	testing	NOUN
fcis-7234	151	19	test	test	NOUN
fcis-7234	151	20	set	set	VERB
fcis-7234	151	21	training	training	NOUN
fcis-7234	151	22	set	set	NOUN
fcis-7234	151	23	srocc	srocc	VERB
fcis-7234	151	24	wadiqam	wadiqam	PROPN
fcis-7234	151	25	-	-	PUNCT
fcis-7234	151	26	nr	nr	PRON
fcis-7234	151	27	ts	ts	PROPN
fcis-7234	151	28	-	-	PUNCT
fcis-7234	151	29	cnn	cnn	PROPN
fcis-7234	151	30	db	db	PROPN
fcis-7234	151	31	-	-	PUNCT
fcis-7234	151	32	cnn	cnn	PROPN
fcis-7234	151	33	pqr	pqr	PROPN
fcis-7234	151	34	hyperiqa	hyperiqa	PROPN
fcis-7234	151	35	method	method	NOUN
fcis-7234	151	36	of	of	ADP
fcis-7234	151	37	this	this	DET
fcis-7234	151	38	article	article	NOUN
fcis-7234	151	39	koniq-10k	koniq-10k	PROPN
fcis-7234	151	40	livec	livec	VERB
fcis-7234	151	41	0.680	0.680	NUM
fcis-7234	151	42	0.652	0.652	NUM
fcis-7234	151	43	0.752	0.752	NUM
fcis-7234	151	44	0.768	0.768	NUM
fcis-7234	151	45	0.782	0.782	NUM
fcis-7234	151	46	0.783	0.783	NUM
fcis-7234	151	47	livec	livec	NOUN
fcis-7234	151	48	koniq-10k	koniq-10k	PROPN
fcis-7234	151	49	0.708	0.708	NUM
fcis-7234	151	50	0.661	0.661	NUM
fcis-7234	151	51	0.751	0.751	NUM
fcis-7234	151	52	0.754	0.754	NUM
fcis-7234	151	53	0.769	0.769	NUM
fcis-7234	151	54	0.770	0.770	NUM
fcis-7234	151	55	according	accord	VERB
fcis-7234	151	56	to	to	ADP
fcis-7234	151	57	the	the	DET
fcis-7234	151	58	data	datum	NOUN
fcis-7234	151	59	shown	show	VERB
fcis-7234	151	60	in	in	ADP
fcis-7234	151	61	table	table	NOUN
fcis-7234	151	62	3	3	NUM
fcis-7234	151	63	,	,	PUNCT
fcis-7234	151	64	the	the	DET
fcis-7234	151	65	experimental	experimental	ADJ
fcis-7234	151	66	results	result	NOUN
fcis-7234	151	67	of	of	ADP
fcis-7234	151	68	the	the	DET
fcis-7234	151	69	two	two	NUM
fcis-7234	151	70	sets	set	NOUN
fcis-7234	151	71	of	of	ADP
fcis-7234	151	72	cross	cross	ADJ
fcis-7234	151	73	-	-	NOUN
fcis-7234	151	74	datasets	dataset	NOUN
fcis-7234	151	75	show	show	VERB
fcis-7234	151	76	that	that	SCONJ
fcis-7234	151	77	the	the	DET
fcis-7234	151	78	proposed	propose	VERB
fcis-7234	151	79	method	method	NOUN
fcis-7234	151	80	in	in	ADP
fcis-7234	151	81	this	this	DET
fcis-7234	151	82	chapter	chapter	NOUN
fcis-7234	151	83	has	have	VERB
fcis-7234	151	84	higher	high	ADJ
fcis-7234	151	85	scores	score	NOUN
fcis-7234	151	86	for	for	ADP
fcis-7234	151	87	both	both	CCONJ
fcis-7234	151	88	srocc	srocc	NOUN
fcis-7234	151	89	and	and	CCONJ
fcis-7234	151	90	plcc	plcc	NOUN
fcis-7234	151	91	metrics	metric	NOUN
fcis-7234	151	92	in	in	ADP
fcis-7234	151	93	real	real	ADJ
fcis-7234	151	94	distortion	distortion	NOUN
fcis-7234	151	95	image	image	NOUN
fcis-7234	151	96	testing	testing	NOUN
fcis-7234	151	97	,	,	PUNCT
fcis-7234	151	98	indicating	indicate	VERB
fcis-7234	151	99	that	that	SCONJ
fcis-7234	151	100	the	the	DET
fcis-7234	151	101	method	method	NOUN
fcis-7234	151	102	has	have	VERB
fcis-7234	151	103	better	well	ADJ
fcis-7234	151	104	generalization	generalization	NOUN
fcis-7234	151	105	performance	performance	NOUN
fcis-7234	151	106	and	and	CCONJ
fcis-7234	151	107	can	can	AUX
fcis-7234	151	108	be	be	AUX
fcis-7234	151	109	applied	apply	VERB
fcis-7234	151	110	to	to	ADP
fcis-7234	151	111	real	real	ADJ
fcis-7234	151	112	-	-	PUNCT
fcis-7234	151	113	life	life	NOUN
fcis-7234	151	114	distortion	distortion	NOUN
fcis-7234	151	115	scenes	scene	NOUN
fcis-7234	151	116	.	.	PUNCT
fcis-7234	152	1	5	5	X
fcis-7234	152	2	.	.	X
fcis-7234	152	3	concluding	conclude	VERB
fcis-7234	152	4	remarks	remark	NOUN
fcis-7234	152	5	to	to	PART
fcis-7234	152	6	solve	solve	VERB
fcis-7234	152	7	the	the	DET
fcis-7234	152	8	problem	problem	NOUN
fcis-7234	152	9	that	that	PRON
fcis-7234	152	10	nr	nr	PROPN
fcis-7234	152	11	-	-	PROPN
fcis-7234	152	12	iqa	iqa	PROPN
fcis-7234	152	13	can	can	AUX
fcis-7234	152	14	not	not	PART
fcis-7234	152	15	simulate	simulate	VERB
fcis-7234	152	16	the	the	DET
fcis-7234	152	17	hsv	hsv	NOUN
fcis-7234	152	18	process	process	NOUN
fcis-7234	152	19	,	,	PUNCT
fcis-7234	152	20	this	this	DET
fcis-7234	152	21	paper	paper	NOUN
fcis-7234	152	22	adopts	adopt	VERB
fcis-7234	152	23	methods	method	NOUN
fcis-7234	152	24	such	such	ADJ
fcis-7234	152	25	as	as	ADP
fcis-7234	152	26	improving	improve	VERB
fcis-7234	152	27	the	the	DET
fcis-7234	152	28	u	u	ADJ
fcis-7234	152	29	-	-	ADJ
fcis-7234	152	30	net	net	ADJ
fcis-7234	152	31	structure	structure	NOUN
fcis-7234	152	32	and	and	CCONJ
fcis-7234	152	33	introducing	introduce	VERB
fcis-7234	152	34	the	the	DET
fcis-7234	152	35	channel	channel	NOUN
fcis-7234	152	36	attention	attention	NOUN
fcis-7234	152	37	mechanism	mechanism	NOUN
fcis-7234	152	38	senet	senet	NOUN
fcis-7234	152	39	structure	structure	NOUN
fcis-7234	152	40	to	to	PART
fcis-7234	152	41	improve	improve	VERB
fcis-7234	152	42	the	the	DET
fcis-7234	152	43	accuracy	accuracy	NOUN
fcis-7234	152	44	of	of	ADP
fcis-7234	152	45	the	the	DET
fcis-7234	152	46	generated	generate	VERB
fcis-7234	152	47	pictures	picture	NOUN
fcis-7234	152	48	.	.	PUNCT
fcis-7234	153	1	also	also	ADV
fcis-7234	153	2	,	,	PUNCT
fcis-7234	153	3	this	this	DET
fcis-7234	153	4	paper	paper	NOUN
fcis-7234	153	5	incorporates	incorporate	VERB
fcis-7234	153	6	a	a	DET
fcis-7234	153	7	feature	feature	NOUN
fcis-7234	153	8	similarity	similarity	NOUN
fcis-7234	153	9	measurement	measurement	NOUN
fcis-7234	153	10	system	system	NOUN
fcis-7234	153	11	to	to	PART
fcis-7234	153	12	generate	generate	VERB
fcis-7234	153	13	pseudo	pseudo	NOUN
fcis-7234	153	14	-	-	ADJ
fcis-7234	153	15	fsim	fsim	ADJ
fcis-7234	153	16	and	and	CCONJ
fcis-7234	153	17	fsim	fsim	ADJ
fcis-7234	153	18	maps	map	NOUN
fcis-7234	153	19	,	,	PUNCT
fcis-7234	153	20	and	and	CCONJ
fcis-7234	153	21	a	a	DET
fcis-7234	153	22	dual	dual	ADJ
fcis-7234	153	23	discriminator	discriminator	NOUN
fcis-7234	153	24	structure	structure	NOUN
fcis-7234	153	25	to	to	PART
fcis-7234	153	26	improve	improve	VERB
fcis-7234	153	27	the	the	DET
fcis-7234	153	28	performance	performance	NOUN
fcis-7234	153	29	of	of	ADP
fcis-7234	153	30	the	the	DET
fcis-7234	153	31	discriminator	discriminator	NOUN
fcis-7234	153	32	.	.	PUNCT
fcis-7234	154	1	to	to	PART
fcis-7234	154	2	improve	improve	VERB
fcis-7234	154	3	the	the	DET
fcis-7234	154	4	prediction	prediction	NOUN
fcis-7234	154	5	accuracy	accuracy	NOUN
fcis-7234	154	6	and	and	CCONJ
fcis-7234	154	7	generalization	generalization	NOUN
fcis-7234	154	8	of	of	ADP
fcis-7234	154	9	the	the	DET
fcis-7234	154	10	assessment	assessment	NOUN
fcis-7234	154	11	network	network	NOUN
fcis-7234	154	12	of	of	ADP
fcis-7234	154	13	image	image	NOUN
fcis-7234	154	14	quality	quality	NOUN
fcis-7234	154	15	.	.	PUNCT
fcis-7234	155	1	in	in	ADP
fcis-7234	155	2	this	this	DET
fcis-7234	155	3	paper	paper	NOUN
fcis-7234	155	4	,	,	PUNCT
fcis-7234	155	5	we	we	PRON
fcis-7234	155	6	propose	propose	VERB
fcis-7234	155	7	a	a	DET
fcis-7234	155	8	multi	multi	ADJ
fcis-7234	155	9	-	-	ADJ
fcis-7234	155	10	scale	scale	ADJ
fcis-7234	155	11	feature	feature	NOUN
fcis-7234	155	12	extractor	extractor	NOUN
fcis-7234	155	13	scheme	scheme	NOUN
fcis-7234	155	14	to	to	PART
fcis-7234	155	15	generate	generate	VERB
fcis-7234	155	16	images	image	NOUN
fcis-7234	155	17	at	at	ADP
fcis-7234	155	18	different	different	ADJ
fcis-7234	155	19	scales	scale	NOUN
fcis-7234	155	20	by	by	ADP
fcis-7234	155	21	image	image	NOUN
fcis-7234	155	22	pyramids	pyramid	NOUN
fcis-7234	155	23	and	and	CCONJ
fcis-7234	155	24	apply	apply	VERB
fcis-7234	155	25	feature	feature	NOUN
fcis-7234	155	26	extractors	extractor	NOUN
fcis-7234	155	27	at	at	ADP
fcis-7234	155	28	each	each	DET
fcis-7234	155	29	level	level	NOUN
fcis-7234	155	30	to	to	PART
fcis-7234	155	31	extract	extract	VERB
fcis-7234	155	32	features	feature	NOUN
fcis-7234	155	33	in	in	ADP
fcis-7234	155	34	order	order	NOUN
fcis-7234	155	35	to	to	PART
fcis-7234	155	36	obtain	obtain	VERB
fcis-7234	155	37	feature	feature	NOUN
fcis-7234	155	38	vector	vector	NOUN
fcis-7234	155	39	information	information	NOUN
fcis-7234	155	40	at	at	ADP
fcis-7234	155	41	different	different	ADJ
fcis-7234	155	42	scales	scale	NOUN
fcis-7234	155	43	to	to	PART
fcis-7234	155	44	detect	detect	VERB
fcis-7234	155	45	overall	overall	ADJ
fcis-7234	155	46	and	and	CCONJ
fcis-7234	155	47	detailed	detailed	ADJ
fcis-7234	155	48	distortions	distortion	NOUN
fcis-7234	155	49	.	.	PUNCT
fcis-7234	156	1	the	the	DET
fcis-7234	156	2	improved	improved	ADJ
fcis-7234	156	3	method	method	NOUN
fcis-7234	156	4	proposed	propose	VERB
fcis-7234	156	5	in	in	ADP
fcis-7234	156	6	this	this	DET
fcis-7234	156	7	paper	paper	NOUN
fcis-7234	156	8	is	be	AUX
fcis-7234	156	9	effective	effective	ADJ
fcis-7234	156	10	in	in	ADP
fcis-7234	156	11	improving	improve	VERB
fcis-7234	156	12	the	the	DET
fcis-7234	156	13	prediction	prediction	NOUN
fcis-7234	156	14	accuracy	accuracy	NOUN
fcis-7234	156	15	and	and	CCONJ
fcis-7234	156	16	also	also	ADV
fcis-7234	156	17	has	have	VERB
fcis-7234	156	18	a	a	DET
fcis-7234	156	19	high	high	ADJ
fcis-7234	156	20	degree	degree	NOUN
fcis-7234	156	21	of	of	ADP
fcis-7234	156	22	similarity	similarity	NOUN
fcis-7234	156	23	with	with	ADP
fcis-7234	156	24	the	the	DET
fcis-7234	156	25	perceptual	perceptual	ADJ
fcis-7234	156	26	consistency	consistency	NOUN
fcis-7234	156	27	of	of	ADP
fcis-7234	156	28	the	the	DET
fcis-7234	156	29	human	human	ADJ
fcis-7234	156	30	visual	visual	ADJ
fcis-7234	156	31	system	system	NOUN
fcis-7234	156	32	.	.	PUNCT
fcis-7234	157	1	however	however	ADV
fcis-7234	157	2	,	,	PUNCT
fcis-7234	157	3	we	we	PRON
fcis-7234	157	4	still	still	ADV
fcis-7234	157	5	need	need	VERB
fcis-7234	157	6	to	to	PART
fcis-7234	157	7	face	face	VERB
fcis-7234	157	8	some	some	DET
fcis-7234	157	9	problems	problem	NOUN
fcis-7234	157	10	.	.	PUNCT
fcis-7234	158	1	due	due	ADP
fcis-7234	158	2	to	to	ADP
fcis-7234	158	3	the	the	DET
fcis-7234	158	4	richness	richness	NOUN
fcis-7234	158	5	of	of	ADP
fcis-7234	158	6	image	image	NOUN
fcis-7234	158	7	distortion	distortion	NOUN
fcis-7234	158	8	types	type	NOUN
fcis-7234	158	9	,	,	PUNCT
fcis-7234	158	10	each	each	PRON
fcis-7234	158	11	with	with	ADP
fcis-7234	158	12	different	different	ADJ
fcis-7234	158	13	characteristics	characteristic	NOUN
fcis-7234	158	14	,	,	PUNCT
fcis-7234	158	15	but	but	CCONJ
fcis-7234	158	16	according	accord	VERB
fcis-7234	158	17	to	to	ADP
fcis-7234	158	18	the	the	DET
fcis-7234	158	19	available	available	ADJ
fcis-7234	158	20	literature	literature	NOUN
fcis-7234	158	21	reports	report	NOUN
fcis-7234	158	22	and	and	CCONJ
fcis-7234	158	23	experimental	experimental	ADJ
fcis-7234	158	24	results	result	NOUN
fcis-7234	158	25	,	,	PUNCT
fcis-7234	158	26	there	there	PRON
fcis-7234	158	27	does	do	AUX
fcis-7234	158	28	not	not	PART
fcis-7234	158	29	exist	exist	VERB
fcis-7234	158	30	an	an	DET
fcis-7234	158	31	image	image	NOUN
fcis-7234	158	32	quality	quality	NOUN
fcis-7234	158	33	assessment	assessment	NOUN
fcis-7234	158	34	algorithm	algorithm	NOUN
fcis-7234	158	35	that	that	PRON
fcis-7234	158	36	can	can	AUX
fcis-7234	158	37	show	show	VERB
fcis-7234	158	38	advantages	advantage	NOUN
fcis-7234	158	39	over	over	ADP
fcis-7234	158	40	other	other	ADJ
fcis-7234	158	41	algorithms	algorithm	NOUN
fcis-7234	158	42	on	on	ADP
fcis-7234	158	43	all	all	DET
fcis-7234	158	44	distortion	distortion	NOUN
fcis-7234	158	45	types	type	NOUN
fcis-7234	158	46	.	.	PUNCT
fcis-7234	159	1	therefore	therefore	ADV
fcis-7234	159	2	,	,	PUNCT
fcis-7234	159	3	future	future	ADJ
fcis-7234	159	4	research	research	NOUN
fcis-7234	159	5	should	should	AUX
fcis-7234	159	6	focus	focus	VERB
fcis-7234	159	7	on	on	ADP
fcis-7234	159	8	the	the	DET
fcis-7234	159	9	design	design	NOUN
fcis-7234	159	10	of	of	ADP
fcis-7234	159	11	general	general	ADJ
fcis-7234	159	12	-	-	PUNCT
fcis-7234	159	13	purpose	purpose	NOUN
fcis-7234	159	14	and	and	CCONJ
fcis-7234	159	15	high	high	ADJ
fcis-7234	159	16	-	-	PUNCT
fcis-7234	159	17	performance	performance	NOUN
fcis-7234	159	18	image	image	NOUN
fcis-7234	159	19	quality	quality	NOUN
fcis-7234	159	20	assessment	assessment	NOUN
fcis-7234	159	21	algorithms	algorithm	NOUN
fcis-7234	159	22	.	.	PUNCT
fcis-7234	160	1	only	only	ADV
fcis-7234	160	2	in	in	ADP
fcis-7234	160	3	this	this	DET
fcis-7234	160	4	way	way	NOUN
fcis-7234	160	5	can	can	AUX
fcis-7234	160	6	we	we	PRON
fcis-7234	160	7	meet	meet	VERB
fcis-7234	160	8	the	the	DET
fcis-7234	160	9	needs	need	NOUN
fcis-7234	160	10	of	of	ADP
fcis-7234	160	11	different	different	ADJ
fcis-7234	160	12	application	application	NOUN
fcis-7234	160	13	scenarios	scenario	NOUN
fcis-7234	160	14	and	and	CCONJ
fcis-7234	160	15	provide	provide	VERB
fcis-7234	160	16	more	more	ADV
fcis-7234	160	17	comprehensive	comprehensive	ADJ
fcis-7234	160	18	and	and	CCONJ
fcis-7234	160	19	accurate	accurate	ADJ
fcis-7234	160	20	image	image	NOUN
fcis-7234	160	21	quality	quality	NOUN
fcis-7234	160	22	assessment	assessment	NOUN
fcis-7234	160	23	services	service	NOUN
fcis-7234	160	24	.	.	PUNCT
fcis-7234	161	1	references	reference	NOUN
fcis-7234	161	2	[	[	X
fcis-7234	161	3	1	1	NUM
fcis-7234	161	4	]	]	SYM
fcis-7234	161	5	v	v	X
fcis-7234	161	6	kumar	kumar	PROPN
fcis-7234	161	7	,	,	PUNCT
fcis-7234	161	8	bawa	bawa	PROPN
fcis-7234	161	9	v	v	ADP
fcis-7234	161	10	s.	s.	PROPN
fcis-7234	161	11	no	no	DET
fcis-7234	161	12	reference	reference	NOUN
fcis-7234	161	13	image	image	NOUN
fcis-7234	161	14	quality	quality	NOUN
fcis-7234	161	15	assessment	assessment	NOUN
fcis-7234	161	16	metric	metric	NOUN
fcis-7234	161	17	based	base	VERB
fcis-7234	161	18	on	on	ADP
fcis-7234	161	19	regional	regional	ADJ
fcis-7234	161	20	mutual	mutual	ADJ
fcis-7234	161	21	information	information	NOUN
fcis-7234	161	22	among	among	ADP
fcis-7234	161	23	images[j	images[j	PROPN
fcis-7234	161	24	]	]	PUNCT
fcis-7234	161	25	.	.	PUNCT
fcis-7234	161	26	2019	2019	NUM
fcis-7234	161	27	.	.	PUNCT
fcis-7234	162	1	[	[	X
fcis-7234	162	2	2	2	NUM
fcis-7234	162	3	]	]	X
fcis-7234	162	4	oszust	oszust	ADJ
fcis-7234	162	5	m.	m.	NOUN
fcis-7234	162	6	local	local	ADJ
fcis-7234	162	7	feature	feature	NOUN
fcis-7234	162	8	descriptor	descriptor	NOUN
fcis-7234	162	9	and	and	CCONJ
fcis-7234	162	10	derivative	derivative	ADJ
fcis-7234	162	11	filters	filter	NOUN
fcis-7234	162	12	for	for	ADP
fcis-7234	162	13	blind	blind	ADJ
fcis-7234	162	14	image	image	NOUN
fcis-7234	162	15	quality	quality	NOUN
fcis-7234	162	16	assessment[j	assessment[j	X
fcis-7234	162	17	]	]	PUNCT
fcis-7234	162	18	.	.	PUNCT
fcis-7234	163	1	ieee	ieee	PROPN
fcis-7234	163	2	signal	signal	NOUN
fcis-7234	163	3	processing	processing	NOUN
fcis-7234	163	4	letters	letter	NOUN
fcis-7234	163	5	,	,	PUNCT
fcis-7234	163	6	2019:1	2019:1	NUM
fcis-7234	163	7	-	-	SYM
fcis-7234	163	8	1	1	NUM
fcis-7234	163	9	.	.	PUNCT
fcis-7234	164	1	[	[	X
fcis-7234	164	2	3	3	X
fcis-7234	164	3	]	]	X
fcis-7234	164	4	liu	liu	PROPN
fcis-7234	164	5	y	y	PROPN
fcis-7234	164	6	,	,	PUNCT
fcis-7234	164	7	gu	gu	PROPN
fcis-7234	164	8	k	k	PROPN
fcis-7234	164	9	,	,	PUNCT
fcis-7234	164	10	zhang	zhang	PROPN
fcis-7234	164	11	y	y	PROPN
fcis-7234	164	12	,	,	PUNCT
fcis-7234	164	13	et	et	PROPN
fcis-7234	164	14	al	al	PROPN
fcis-7234	164	15	.	.	PUNCT
fcis-7234	165	1	unsupervised	unsupervised	ADJ
fcis-7234	165	2	blind	blind	ADJ
fcis-7234	165	3	image	image	NOUN
fcis-7234	165	4	quality	quality	NOUN
fcis-7234	165	5	assessment	assessment	NOUN
fcis-7234	165	6	via	via	ADP
fcis-7234	165	7	statistical	statistical	ADJ
fcis-7234	165	8	measurements	measurement	NOUN
fcis-7234	165	9	of	of	ADP
fcis-7234	165	10	structure	structure	NOUN
fcis-7234	165	11	,	,	PUNCT
fcis-7234	165	12	naturalness	naturalness	NOUN
fcis-7234	165	13	,	,	PUNCT
fcis-7234	165	14	and	and	CCONJ
fcis-7234	165	15	perception[j	perception[j	PROPN
fcis-7234	165	16	]	]	PUNCT
fcis-7234	165	17	.	.	PUNCT
fcis-7234	166	1	ieee	ieee	NOUN
fcis-7234	166	2	transactions	transaction	NOUN
fcis-7234	166	3	on	on	ADP
fcis-7234	166	4	circuits	circuit	NOUN
fcis-7234	166	5	and	and	CCONJ
fcis-7234	166	6	systems	system	NOUN
fcis-7234	166	7	for	for	ADP
fcis-7234	166	8	video	video	NOUN
fcis-7234	166	9	technology	technology	NOUN
fcis-7234	166	10	,	,	PUNCT
fcis-7234	166	11	2020	2020	NUM
fcis-7234	166	12	,	,	PUNCT
fcis-7234	166	13	30(4):929	30(4):929	NOUN
fcis-7234	166	14	-	-	SYM
fcis-7234	166	15	943	943	NUM
fcis-7234	166	16	.	.	PUNCT
fcis-7234	167	1	[	[	X
fcis-7234	167	2	4	4	NUM
fcis-7234	167	3	]	]	X
fcis-7234	167	4	bosse	bosse	PROPN
fcis-7234	167	5	s	s	PART
fcis-7234	167	6	,	,	PUNCT
fcis-7234	167	7	maniry	maniry	PROPN
fcis-7234	167	8	d	d	PROPN
fcis-7234	167	9	,	,	PUNCT
fcis-7234	167	10	muller	muller	PROPN
fcis-7234	167	11	k	k	PROPN
fcis-7234	167	12	r	r	PROPN
fcis-7234	167	13	,	,	PUNCT
fcis-7234	167	14	et	et	PROPN
fcis-7234	167	15	al	al	PROPN
fcis-7234	167	16	.	.	PUNCT
fcis-7234	168	1	deep	deep	ADJ
fcis-7234	168	2	neural	neural	ADJ
fcis-7234	168	3	networks	network	NOUN
fcis-7234	168	4	for	for	ADP
fcis-7234	168	5	no	no	DET
fcis-7234	168	6	-	-	PUNCT
fcis-7234	168	7	reference	reference	NOUN
fcis-7234	168	8	and	and	CCONJ
fcis-7234	168	9	full	full	ADJ
fcis-7234	168	10	-	-	PUNCT
fcis-7234	168	11	reference	reference	NOUN
fcis-7234	168	12	image	image	NOUN
fcis-7234	168	13	quality	quality	NOUN
fcis-7234	168	14	assessment[j	assessment[j	X
fcis-7234	168	15	]	]	PUNCT
fcis-7234	168	16	.	.	PUNCT
fcis-7234	169	1	ieee	ieee	NOUN
fcis-7234	169	2	transactions	transaction	NOUN
fcis-7234	169	3	on	on	ADP
fcis-7234	169	4	image	image	NOUN
fcis-7234	169	5	processing	processing	NOUN
fcis-7234	169	6	,	,	PUNCT
fcis-7234	169	7	2017:1	2017:1	NUM
fcis-7234	169	8	-	-	SYM
fcis-7234	169	9	1	1	NUM
fcis-7234	169	10	.	.	NUM
fcis-7234	169	11	63	63	NUM
fcis-7234	170	1	[	[	SYM
fcis-7234	170	2	5	5	NUM
fcis-7234	170	3	]	]	PUNCT
fcis-7234	170	4	kang	kang	PROPN
fcis-7234	170	5	l	l	PROPN
fcis-7234	170	6	,	,	PUNCT
fcis-7234	170	7	ye	ye	PRON
fcis-7234	170	8	p	p	NOUN
fcis-7234	170	9	,	,	PUNCT
fcis-7234	170	10	li	li	PROPN
fcis-7234	170	11	y	y	PROPN
fcis-7234	170	12	,	,	PUNCT
fcis-7234	170	13	doermann	doermann	PROPN
fcis-7234	170	14	d.	d.	PROPN
fcis-7234	170	15	convolutional	convolutional	ADJ
fcis-7234	170	16	neural	neural	ADJ
fcis-7234	170	17	networks	network	NOUN
fcis-7234	170	18	for	for	ADP
fcis-7234	170	19	no	no	DET
fcis-7234	170	20	-	-	PUNCT
fcis-7234	170	21	reference	reference	NOUN
fcis-7234	170	22	image	image	NOUN
fcis-7234	170	23	quality	quality	NOUN
fcis-7234	170	24	assessment[c]//	assessment[c]//	PROPN
fcis-7234	170	25	2014	2014	NUM
fcis-7234	170	26	ieee	ieee	NOUN
fcis-7234	170	27	conference	conference	NOUN
fcis-7234	170	28	on	on	ADP
fcis-7234	170	29	computer	computer	NOUN
fcis-7234	170	30	vision	vision	NOUN
fcis-7234	170	31	and	and	CCONJ
fcis-7234	170	32	pattern	pattern	NOUN
fcis-7234	170	33	recognition	recognition	NOUN
fcis-7234	170	34	.	.	PUNCT
fcis-7234	171	1	ieee	ieee	PROPN
fcis-7234	171	2	,	,	PUNCT
fcis-7234	171	3	2014	2014	NUM
fcis-7234	171	4	.	.	PUNCT
fcis-7234	172	1	[	[	X
fcis-7234	172	2	6	6	NUM
fcis-7234	172	3	]	]	X
fcis-7234	172	4	bianco	bianco	PROPN
fcis-7234	172	5	s	s	PROPN
fcis-7234	172	6	,	,	PUNCT
fcis-7234	172	7	celona	celona	PROPN
fcis-7234	172	8	l	l	NOUN
fcis-7234	172	9	,	,	PUNCT
fcis-7234	172	10	napoletano	napoletano	PROPN
fcis-7234	172	11	p	p	X
fcis-7234	172	12	,	,	PUNCT
fcis-7234	172	13	et	et	PROPN
fcis-7234	172	14	al	al	PROPN
fcis-7234	172	15	.	.	PROPN
fcis-7234	173	1	on	on	ADP
fcis-7234	173	2	the	the	DET
fcis-7234	173	3	use	use	NOUN
fcis-7234	173	4	of	of	ADP
fcis-7234	173	5	deep	deep	ADJ
fcis-7234	173	6	learning	learning	NOUN
fcis-7234	173	7	for	for	ADP
fcis-7234	173	8	blind	blind	ADJ
fcis-7234	173	9	image	image	NOUN
fcis-7234	173	10	quality	quality	NOUN
fcis-7234	173	11	assessment[j	assessment[j	X
fcis-7234	173	12	]	]	PUNCT
fcis-7234	173	13	.	.	PUNCT
fcis-7234	174	1	signal	signal	PROPN
fcis-7234	174	2	image	image	NOUN
fcis-7234	174	3	&	&	CCONJ
fcis-7234	174	4	video	video	NOUN
fcis-7234	174	5	processing	processing	NOUN
fcis-7234	174	6	,	,	PUNCT
fcis-7234	174	7	2016	2016	NUM
fcis-7234	174	8	.	.	PUNCT
fcis-7234	175	1	[	[	X
fcis-7234	175	2	7	7	X
fcis-7234	175	3	]	]	X
fcis-7234	175	4	lin	lin	PROPN
fcis-7234	175	5	k	k	PROPN
fcis-7234	175	6	y	y	PROPN
fcis-7234	175	7	,	,	PUNCT
fcis-7234	175	8	wang	wang	PROPN
fcis-7234	175	9	g.	g.	PROPN
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fcis-7234	175	11	-	-	PUNCT
fcis-7234	175	12	iqa	iqa	PROPN
fcis-7234	175	13	:	:	PUNCT
fcis-7234	175	14	no	no	DET
fcis-7234	175	15	-	-	PUNCT
fcis-7234	175	16	reference	reference	NOUN
fcis-7234	175	17	image	image	NOUN
fcis-7234	175	18	quality	quality	NOUN
fcis-7234	175	19	assessment	assessment	NOUN
fcis-7234	175	20	via	via	ADP
fcis-7234	175	21	adversarial	adversarial	ADJ
fcis-7234	175	22	learning[c]//	learning[c]//	PROPN
fcis-7234	175	23	2018	2018	NUM
fcis-7234	175	24	ieee	ieee	NOUN
fcis-7234	175	25	/	/	SYM
fcis-7234	175	26	cvf	cvf	NOUN
fcis-7234	175	27	conference	conference	NOUN
fcis-7234	175	28	on	on	ADP
fcis-7234	175	29	computer	computer	NOUN
fcis-7234	175	30	vision	vision	NOUN
fcis-7234	175	31	and	and	CCONJ
fcis-7234	175	32	pattern	pattern	NOUN
fcis-7234	175	33	recognition	recognition	NOUN
fcis-7234	175	34	(	(	PUNCT
fcis-7234	175	35	cvpr	cvpr	NOUN
fcis-7234	175	36	)	)	PUNCT
fcis-7234	175	37	.	.	PUNCT
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fcis-7234	176	2	,	,	PUNCT
fcis-7234	176	3	2018	2018	NUM
fcis-7234	176	4	.	.	PUNCT
fcis-7234	177	1	[	[	X
fcis-7234	177	2	8	8	NUM
fcis-7234	177	3	]	]	X
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fcis-7234	177	5	h	h	PROPN
fcis-7234	177	6	,	,	PUNCT
fcis-7234	177	7	chen	chen	PROPN
fcis-7234	177	8	d	d	PROPN
fcis-7234	177	9	,	,	PUNCT
fcis-7234	177	10	wang	wang	PROPN
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fcis-7234	178	1	ran4iqa	ran4iqa	NOUN
fcis-7234	178	2	:	:	PUNCT
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fcis-7234	178	4	adversarial	adversarial	ADJ
fcis-7234	178	5	nets	net	NOUN
fcis-7234	178	6	for	for	ADP
fcis-7234	178	7	no	no	DET
fcis-7234	178	8	-	-	PUNCT
fcis-7234	178	9	reference	reference	NOUN
fcis-7234	178	10	image	image	NOUN
fcis-7234	178	11	quality	quality	NOUN
fcis-7234	178	12	assessment[j	assessment[j	X
fcis-7234	178	13	]	]	X
fcis-7234	178	14	.	.	PUNCT
fcis-7234	179	1	2017	2017	NUM
fcis-7234	179	2	.	.	PUNCT
fcis-7234	180	1	[	[	X
fcis-7234	180	2	9	9	NUM
fcis-7234	180	3	]	]	X
fcis-7234	180	4	pan	pan	NOUN
fcis-7234	180	5	d	d	PROPN
fcis-7234	180	6	,	,	PUNCT
fcis-7234	180	7	shi	shi	PROPN
fcis-7234	180	8	p	p	X
fcis-7234	180	9	,	,	PUNCT
fcis-7234	180	10	hou	hou	PROPN
fcis-7234	180	11	m	m	PROPN
fcis-7234	180	12	,	,	PUNCT
fcis-7234	180	13	et	et	PROPN
fcis-7234	180	14	al	al	PROPN
fcis-7234	180	15	.	.	PROPN
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fcis-7234	180	17	predicting	predict	VERB
fcis-7234	180	18	similar	similar	ADJ
fcis-7234	180	19	quality	quality	NOUN
fcis-7234	180	20	map	map	NOUN
fcis-7234	180	21	for	for	ADP
fcis-7234	180	22	image	image	NOUN
fcis-7234	180	23	quality	quality	NOUN
fcis-7234	180	24	assessment[c]//proceedings	assessment[c]//proceeding	NOUN
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fcis-7234	180	26	the	the	DET
fcis-7234	180	27	ieee	ieee	NOUN
fcis-7234	180	28	conference	conference	NOUN
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fcis-7234	180	30	computer	computer	NOUN
fcis-7234	180	31	vision	vision	NOUN
fcis-7234	180	32	and	and	CCONJ
fcis-7234	180	33	pattern	pattern	NOUN
fcis-7234	180	34	recognition	recognition	NOUN
fcis-7234	180	35	.	.	PUNCT
fcis-7234	181	1	2018	2018	NUM
fcis-7234	181	2	:	:	PUNCT
fcis-7234	181	3	6373	6373	NUM
fcis-7234	181	4	-	-	SYM
fcis-7234	181	5	6382	6382	NUM
fcis-7234	181	6	.	.	PUNCT
fcis-7234	182	1	[	[	X
fcis-7234	182	2	10	10	NUM
fcis-7234	182	3	]	]	X
fcis-7234	182	4	cao	cao	PROPN
fcis-7234	182	5	yudong	yudong	PROPN
fcis-7234	182	6	,	,	PUNCT
fcis-7234	182	7	cai	cai	PROPN
fcis-7234	182	8	xibiao	xibiao	PROPN
fcis-7234	182	9	reference	reference	PROPN
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fcis-7234	182	11	image	image	NOUN
fcis-7234	182	12	quality	quality	NOUN
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fcis-7234	182	17	enhanced	enhance	VERB
fcis-7234	182	18	adversarial	adversarial	ADJ
fcis-7234	182	19	learning	learning	NOUN
fcis-7234	182	20	[	[	X
fcis-7234	182	21	j	j	X
fcis-7234	182	22	]	]	X
fcis-7234	182	23	computer	computer	NOUN
fcis-7234	182	24	applications	application	NOUN
fcis-7234	182	25	,	,	PUNCT
fcis-7234	182	26	2020	2020	NUM
fcis-7234	182	27	,	,	PUNCT
fcis-7234	182	28	v.40	v.40	ADP
fcis-7234	182	29	;	;	PUNCT
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fcis-7234	182	31	-	-	PUNCT
fcis-7234	182	32	77	77	NUM
fcis-7234	182	33	.	.	PUNCT
fcis-7234	183	1	[	[	X
fcis-7234	183	2	11	11	NUM
fcis-7234	183	3	]	]	PUNCT
fcis-7234	183	4	ronneberger	ronneberger	NOUN
fcis-7234	183	5	o	o	NOUN
fcis-7234	183	6	,	,	PUNCT
fcis-7234	183	7	fischer	fischer	PROPN
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fcis-7234	183	9	,	,	PUNCT
fcis-7234	183	10	brox	brox	PROPN
fcis-7234	183	11	t.	t.	PROPN
fcis-7234	183	12	u	u	PROPN
fcis-7234	183	13	-	-	NOUN
fcis-7234	183	14	net	net	ADJ
fcis-7234	183	15	:	:	PUNCT
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fcis-7234	183	17	networks	network	NOUN
fcis-7234	183	18	for	for	ADP
fcis-7234	183	19	biomedical	biomedical	ADJ
fcis-7234	183	20	image	image	NOUN
fcis-7234	183	21	segmentation[j	segmentation[j	PROPN
fcis-7234	183	22	]	]	PUNCT
fcis-7234	183	23	.	.	PUNCT
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fcis-7234	184	2	conference	conference	NOUN
fcis-7234	184	3	on	on	ADP
fcis-7234	184	4	medical	medical	ADJ
fcis-7234	184	5	image	image	NOUN
fcis-7234	184	6	computing	computing	NOUN
fcis-7234	184	7	and	and	CCONJ
fcis-7234	184	8	computerassisted	computerassiste	VERB
fcis-7234	184	9	intervention	intervention	NOUN
fcis-7234	184	10	,	,	PUNCT
fcis-7234	184	11	2015	2015	NUM
fcis-7234	184	12	:	:	PUNCT
fcis-7234	184	13	234	234	NUM
fcis-7234	184	14	-	-	SYM
fcis-7234	184	15	241	241	NUM
fcis-7234	184	16	.	.	PUNCT
fcis-7234	185	1	[	[	X
fcis-7234	185	2	12	12	NUM
fcis-7234	185	3	]	]	X
fcis-7234	185	4	jv	jv	PROPN
fcis-7234	185	5	a	a	PROPN
fcis-7234	185	6	,	,	PUNCT
fcis-7234	185	7	yqc	yqc	PROPN
fcis-7234	185	8	a	a	PRON
fcis-7234	185	9	,	,	PUNCT
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fcis-7234	185	11	w	w	PROPN
fcis-7234	185	12	b	b	PROPN
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fcis-7234	186	2	:	:	PUNCT
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fcis-7234	186	4	convolutional	convolutional	ADJ
fcis-7234	186	5	generative	generative	ADJ
fcis-7234	186	6	adversarial	adversarial	ADJ
fcis-7234	186	7	network	network	NOUN
fcis-7234	186	8	(	(	PUNCT
fcis-7234	186	9	dcgan	dcgan	NOUN
fcis-7234	186	10	)	)	PUNCT
fcis-7234	186	11	based	base	VERB
fcis-7234	186	12	ballbearing	ballbeare	VERB
fcis-7234	186	13	failure	failure	NOUN
fcis-7234	186	14	detection	detection	NOUN
fcis-7234	186	15	method[j	method[j	NOUN
fcis-7234	186	16	]	]	PUNCT
fcis-7234	186	17	.	.	PUNCT
fcis-7234	187	1	information	information	NOUN
fcis-7234	187	2	sciences	sciences	PROPN
fcis-7234	187	3	,	,	PUNCT
fcis-7234	187	4	2021	2021	NUM
fcis-7234	187	5	,	,	PUNCT
fcis-7234	187	6	542:195	542:195	NOUN
fcis-7234	187	7	-	-	PUNCT
fcis-7234	187	8	211	211	NUM
fcis-7234	187	9	.	.	PUNCT
fcis-7234	188	1	[	[	X
fcis-7234	188	2	13	13	NUM
fcis-7234	188	3	]	]	SYM
fcis-7234	188	4	mao	mao	NOUN
fcis-7234	189	1	c	c	X
fcis-7234	189	2	,	,	PUNCT
fcis-7234	189	3	huang	huang	PROPN
fcis-7234	189	4	l	l	PROPN
fcis-7234	189	5	,	,	PUNCT
fcis-7234	189	6	xiao	xiao	PROPN
fcis-7234	189	7	y	y	PROPN
fcis-7234	189	8	,	,	PUNCT
fcis-7234	189	9	et	et	PROPN
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fcis-7234	189	15	sar	sar	PROPN
fcis-7234	189	16	image	image	PROPN
fcis-7234	189	17	based	base	VERB
fcis-7234	189	18	on	on	ADP
fcis-7234	189	19	cn	cn	PROPN
fcis-7234	189	20	-	-	PUNCT
fcis-7234	189	21	gan	gan	PROPN
fcis-7234	189	22	and	and	CCONJ
fcis-7234	189	23	cnn	cnn	PROPN
fcis-7234	189	24	in	in	ADP
fcis-7234	189	25	complex	complex	ADJ
fcis-7234	189	26	environment[j	environment[j	PROPN
fcis-7234	189	27	]	]	X
fcis-7234	189	28	.	.	PUNCT
fcis-7234	190	1	ieee	ieee	NOUN
fcis-7234	190	2	access	access	NOUN
fcis-7234	190	3	,	,	PUNCT
fcis-7234	190	4	2021	2021	NUM
fcis-7234	190	5	,	,	PUNCT
fcis-7234	190	6	pp	pp	CCONJ
fcis-7234	190	7	(	(	PUNCT
fcis-7234	190	8	99):1	99):1	NUM
fcis-7234	190	9	-	-	SYM
fcis-7234	190	10	1	1	NUM
fcis-7234	190	11	.	.	PUNCT
fcis-7234	191	1	[	[	X
fcis-7234	191	2	14	14	NUM
fcis-7234	191	3	]	]	PUNCT
fcis-7234	191	4	yanding	yanding	PROPN
fcis-7234	191	5	peng	peng	PROPN
fcis-7234	191	6	,	,	PUNCT
fcis-7234	191	7	jiahua	jiahua	PROPN
fcis-7234	191	8	xu	xu	PROPN
fcis-7234	191	9	,	,	PUNCT
fcis-7234	191	10	ziyuan	ziyuan	PROPN
fcis-7234	191	11	luo	luo	PROPN
fcis-7234	191	12	,	,	PUNCT
fcis-7234	191	13	wei	wei	PROPN
fcis-7234	191	14	zhou	zhou	PROPN
fcis-7234	191	15	,	,	PUNCT
fcis-7234	191	16	zhibo	zhibo	PROPN
fcis-7234	191	17	chen	chen	PROPN
fcis-7234	191	18	;	;	PUNCT
fcis-7234	191	19	multi	multi	ADJ
fcis-7234	191	20	-	-	ADJ
fcis-7234	191	21	metric	metric	ADJ
fcis-7234	191	22	fusion	fusion	NOUN
fcis-7234	191	23	network	network	NOUN
fcis-7234	191	24	for	for	ADP
fcis-7234	191	25	image	image	NOUN
fcis-7234	191	26	quality	quality	NOUN
fcis-7234	191	27	assessment	assessment	NOUN
fcis-7234	191	28	.	.	PUNCT
fcis-7234	192	1	proceedings	proceeding	NOUN
fcis-7234	192	2	of	of	ADP
fcis-7234	192	3	the	the	DET
fcis-7234	192	4	ieee	ieee	NOUN
fcis-7234	192	5	/	/	SYM
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fcis-7234	192	8	on	on	ADP
fcis-7234	192	9	computer	computer	NOUN
fcis-7234	192	10	vision	vision	NOUN
fcis-7234	192	11	and	and	CCONJ
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fcis-7234	192	15	cvpr	cvpr	NOUN
fcis-7234	192	16	)	)	PUNCT
fcis-7234	192	17	workshops	workshop	NOUN
fcis-7234	192	18	,	,	PUNCT
fcis-7234	192	19	2021	2021	NUM
fcis-7234	192	20	,	,	PUNCT
fcis-7234	192	21	pp	pp	ADJ
fcis-7234	192	22	.	.	PUNCT
fcis-7234	192	23	18571860	18571860	NUM
fcis-7234	192	24	.	.	PUNCT