IQAWCE.pdf


An Ingenious Application-Specific Quality
Assessment Methods for Compressed Wireless

Capsule Endoscopy Images
Kinde Anlay Fante, Fetulhak Abdurahman and Mulugeta Tegegn Gemeda

Faculty of Electrical and Computer Engineering, Jimma Institute of Technology, Jimma University, Ethiopia

Abstract—Image quality assessment methods are used in
different image processing applications. Among them, image
compression and image super-resolution can be mentioned in
wireless capsule endoscopy (WCE) applications. The existing
image compression algorithms for WCE employ the general-
purpose image quality assessment (IQA) methods to evaluate the
quality of the compressed image. Due to the specific nature of the
images captured by WCE, the general-purpose IQA methods are
not optimal and give less correlated results to that of subjective
IQA (visual perception). This paper presents improved image
quality assessment techniques for wireless capsule endoscopy
applications. The proposed objective IQA methods are obtained
by modifying the existing full-reference image quality assessment
techniques. The modification is done by excluding the non-
informative regions, in endoscopic images, in the computation
of IQA metrics. The experimental results demonstrate that
the proposed IQA method gives an improved peak signal-to-
noise ratio (PSNR) and structural similarity index (SSIM). The
proposed image quality assessment methods are more reliable for
compressed endoscopic capsule images.

Index Terms—Image quality assessment, wireless capsule en-
doscope, compressed images, image compression.

I. INTRODUCTION

Wireless capsule endoscopy (WCE) is a tablet size camera

with a radio frequency transmitter which can be easily ingested

by patients to diagnose gastrointestinal abnormalities [1].

It enables the non-invasive imaging of the gastrointestinal

tract with a high-resolution camera operating in the visible

electromagnetic spectrum. The capsule captures the image of

the inner wall of the human digestive system and transmits it

to a data recorder located outside the patient’s body. The trans-

mission of high-resolution images via a wireless link consumes

high power and requires a high transmission bandwidth [2].

In order to reduce the power consumption and transmission

bandwidth, image compression algorithms are included inside

the capsule [3]. Image processing software on workstation is

used to decompress and process the received images in order to

improve the visual quality. Among the image processing meth-

ods, super-resolution algorithms [4] are proposed to improve

the spatial resolution of the images, image post-processing and

error-correcting codes are proposed to suppress the distortion

due to transmission channel noise [5], and computer-aided

diagnosis systems [6] are used to improve the detection of

abnormalities during the evaluation phase.
Generally, high quality images are required for accurate

diagnosis of abnormalities. Ideally, lossless image compres-

sion methods are needed to retain all information in medical

images. However, the lossless image compression algorithms

achieve a low compression rate. It cannot significantly reduce

the power consumption and the bandwidth requirement of the

WCE transmission system. Hence, lossy image compression

algorithms are preferred to achieve a high compression rate.

The amount of information loss due to the compression

process should not affect the diagnosis outcome [7]. On the

other hand, image compression algorithms that have high

information loss achieve a high compression rate. A good

compromise between the image quality and compression rate

needs to be maintained in this particular application.

In order to design an image compression algorithm that

can achieve a high compression ratio without affecting the

diagnosis accuracy, we need reliable image quality assessment

methods. Such methods can help us to optimize the parameters

of the image compression algorithms, such as quantizer values

and sub-sampling factors [2]. The image quality assessment

methods can be categorized into two groups: the subjective

and objective quality assessment methods [8]. The subjective

quality assessment method is more accurate, time-consuming,

and expensive. Hence, the objective image quality assessment

methods are widely used in practice. The objective image

quality assessment methods are widely used to quantify the

quality of medical images in compression algorithms [2], [3].

Among the popular methods, the peak signal-to-noise ratio

(PSNR) [9], structure similarity index (SSIM) [10], and feature

similarity index (FSIM) [11] were employed to evaluate the

distortion introduced due to the quantization operation of

the endoscopic image compression algorithms. In literature,

a minimum peak signal-to-noise ratio (PSNR) value of 30 dB

has been considered to be sufficient for accurate diagnosis

of pathology in endoscopic images [12]. However, in [7],

[13], a PSNR value of 35 dB was considered as a threshold

value for an accurate diagnosis of abnormalities using medical

images. A reliable objective IQA method for WCE application

will help to preserve the information needed for accurate

diagnosis of abnormalities and optimize the parameters of

image compression algorithms. Some of these IQA methods,

for instance, PSNR and SSIM, give equal importance to all

parts of the image in their evaluation of the image quality

assessment metrics. However, the quality of all the parts of

the image may not be equally important in some applications.

For example, in an endoscopic capsule image, the four cor-

ners of the image are non-informative regions. The pixels in

those regions are all zero. These regions have no important

11



information, and the quality of the pixels in these regions is

unaffected by the image compression algorithms. This makes

the existing objective image quality assessment techniques

less reliable for WCE application, and their correlation with

the subjective assessment techniques is low. In this work, we

present an improved objective image quality assessment meth-

ods that exclude the impact of the non-informative regions.

The proposed ad-hoc algorithm improves the performance of

general-purpose algorithms by incorporating knowledge about

the nature of the images. The incorporated knowledge helps

these algorithms to completely remove the impact of non-

informative regions in the IQA metrics calculation.

The existing objective IQA methods can be classified into

three categories based on the availability of reference im-

ages [14]. These are full reference, reduced reference, and non-

reference IQA methods. The focus of this paper is on full refer-

ence IQA methods for compressed endoscopic capsule images.

The full reference IQA methods can be further divided into

two broad categories. The fist category uses statistical error

metrics such as mean square error (MSE), PSNR, and visual

signal-to-noise ratio (VSNR), etc. And the second category

takes into account the knowledge of the human visual sys-

tem (HVS), which includes FSIM, SSIM, Visual Information

Fidelity (VIF), etc. These IQA metrics are general-purpose

methods and may not be suitable for some applications. An

example is an image taken using a capsule camera, as shown

in Fig. 1. Due to the circular shape of the lens of the capsule

camera and the rectangular shape of the image sensor arrays,

the four corners of the WCE image are occluded from the

illumination source. The corner regions of this image have no

important information. and, all of its pixel values are zero. It

also creates a sharp transition boundary between the corner

regions and the circular visual region of the image. We can

put forward the following two observations from the nature of

endoscopic capsule images. First, the quality of the four corner

regions of endoscopic images is unaffected by lossy image

compression algorithms since all pixel values are zeros. This

affects the statistical error based image quality metrics such

as MSE and PSNR. Second, the sharp transition boundary

between the corner regions (non-informative regions) and

circular visual region of the endoscopic capsule image is

highly affected by lossy image compression algorithms that

involve sub-sampling and quantization such as DCT-based [15]

and DWT-based [16] algorithms. Similarly, the quantization

and subsampling operation in DPCM-based [17], [18], and

error due to inadequate side information in distributed video

coding algorithms [19]. However, this region has no significant

information relevant to the diagnosis of abnormalities in the

gastrointestinal tract. Most of the IQA methods that use the

knowledge about the human visual system, such as FSIM,

SSIM, VIF, etc., estimate the information content of image

regions based on the variance of the image blocks. An image

block that lies in the boundary between the non-informative

regions and the circular visual region has a very high variance.

The presence of this high variance region significantly affects

the reliability of the IQA metrics for this particular domain of

images.

Generally, we can use three types of knowledge in the

design of image quality assessment methods. These are pieces

of knowledge about the human visual system (HVS), statistics

of the image, and the distortion type [14].

The previous works on this topic assessed the suitablity

of the general-purpose image quality assessment methods for

WCE application. Both subjective and objective image quality

assessment methods were presented [20]–[22]. However, the

objective image quality assessment methods for compressed

WCE images [20] have not taken into consideration the pecu-

liar nature of the WCE images. By using the knowledge about

the WCE camera, we developed more reliable image quality

assessment methods. In this work, we use the knowledge about

the statistics of the image acquisition system to improve the

performance of the general-purpose objective IQA methods

for wireless capsule endoscopy applications. The contribution

of this work is that it excludes non-informative regions in the

computation of objective image quality assessment methods

for WCE application. To the best of our knowledge, this the

first work which takes into consideration the peculiar nature of

WCE images in the image quality assessment method for WCE

application. The remaining sections of the paper are organized

as follows. Section II discusses the analysis of the proposed

IQA method. In Section III, the result and discussions are

given. Finally, the conclusion is given in Section IV.

II. ANALYSIS OF THE PROPOSED IMAGE QUALITY

ASSESSMENT METHODS

The proposed IQA algorithm improves the performance of

the general-purpose IQA methods by excluding the following

two regions in the computation of the IQA metrics. These are:

(1) the non-informative (corner) regions, and (2) the boundary

between the corner regions and the circular visual region.

This section will investigate the overall impact of excluding

these two regions in the computation of general-purpose IQA

metrics for WCE application. The block diagram of a full-

Fig. 1. Block diagram of a full-reference objective image quality assessment
method for compressed WCE Images.

reference image quality assessment method is depicted in

Fig. 1. The image is compressed to reduce its size for storage

or transmission. The lossy image compression process intro-

duces distortion to the image. In both DPCM-based [2] and

DCT-based [23] image compression algorithms, the chroma

subsampling and quantization processes introduce distortion.

The compressed image is then decompressed (decoded) for

visualization. The amount of distortion in the decompressed is

quantified using image quality assessment method. The image

quality assessment method takes both the decoded image and

the reference image to compute the objective image quality

assessment metrics.



A. The Effect of Corner Regions

As shown in Fig. 2, the visual region of the capsule camera

is the circular region. For the image of size W × W , the area
of the visual region is πW

2

4
, and the area of the whole image

is W 2. The total area of the non-informative regions, Ani, is
given by:

Ani = W
2(1 − π

4
) (1)

For W = 256, Ani = 14064 pixels. This is about 21.46
% of the total pixels of the image, which constitutes nearly

220, 8 × 8 patches. The total number of pixels in the non-
informative region is more than one-fifth of the total numbers

of pixels of the image, and the impact on the image quality

assessment metrics must be considered. The exclusion of

the pixels in the non-informative regions in the computation

of image quality assessment metrics improves the reliability

of the methods. Several image quality assessment methods

Fig. 2. Wireless capsule endoscopy images [24].

were used to evaluate the performance of wireless capsule

endoscopy image compression algorithms. Among them, mean

squared error (MSE) and peak signal-to-noise ratio (PSNR)

can be mentioned [2]. These methods give equal importance

to all pixels in the image during the computation of quality

assessment metrics. Hence, they are generally not correlated

with subjective image quality assessment methods [25]. They

are used because they are based on the energy of the error sig-

nal (difference in signal between the images being compared),

which is preserved after linear unitary (orthogonal) transforms

such as Discrete Cosine Transform (DCT). The MSE and

PSNR are computed using the following equations [9].

MSE =

∑M,N,3
i=1,j=1,k=1(Xi,j,k − Yi,j,k)2

M × N × 3 (2)

PSNR = 10log10(
2552

MSE
) (3)

Where Xi,j,k is the pixel value of the reference image, Yi,j,k
is the pixel value of the decompressed image, M, N are sizes
of the image, i, j are spatial coordinates of the image array
and k is the index of the color components of a color image.

As described above, the pixel value error in the corner

regions of an endoscopic image during the compression pro-

cess is zero, but the number of pixels shown in equation

(2) includes the corner pixels. This reduces the MSE and

increases the PSNR which makes these methods less corre-

lated with the subjective image quality assessment method.

To alleviate this drawback, the modified versions of mean

squared error (MSE WCE) and peak signal-to-noise ratio
(PSNR WCE) are given below.

MSE WCE =

∑M,N,2
i=0,j=0,k=0(Xi,j,k − Yi,j,k)2
M × N × 3 − 3 × Nc

(4)

Where Nc represents the number of corner pixels.

PSNR WCE = 10log10(
2552

MSE WCE
) (5)

The number of corner pixels, in equation (4), includes those

pixels whose values are unaltered by the image decompression

process. It should be noted that the corner pixel values which

are near to the boundary between the circular visual region

and corner regions are affected by the reconstruction process

of the lossy image decompression algorithms.

Furthermore, the effect of the pixels in the corner regions

on the objective image quality assessment methods can also

be investigated using the structural similarity index (SSIM). It

is given by the following equation [10].

S(X, Y ) =
(2μxμy + C1)(2δxy + C2)

(μ2x + μ
2
y + C1)(δ

2
x + δ

2
y + C2)

(6)

Where μx, μy are the mean values, δx, δy are the unbiased
standard deviations of the reference and distorted images

respectively, and C1, C2 are constants. From equation (6), we
can conclude that the presence of zero-value corner pixels

reduces the mean values and increases the values of variances

in both the reference and distorted images. Moreover, the

corner regions have no structural units that can be affected by

compression algorithms. Hence, the structures in the decom-

pressed and original images are similar in the corner regions

of endoscopic images. This tends to increase the SSIM value,

which is misleading.

To eliminate the effect of the corner regions, the pixel

values in the circular visual regions only are included in the

computation of IQA metrics. We clip the corner regions and

compute the metrics. Overall, the presence of corner regions

in capsule endoscopic images increases both PSNR and SSIM

values during the evaluation of the quality metrics of the image

compression algorithms. Hence, the exclusion of the corner

regions in the image quality assessment metrics computation

of wireless capsule endoscopy images makes the metrics more

reliable. This is due to the fact that non-informative regions are

not affected by the image compression process, and it increases

the image quality scores of the IQA methods.

B. The Effect of Boundary Region

Unlike natural image boundaries, the boundary between

corner regions and the circular visual region has a sharp

pixel value transition. Consequently, it contains large high-

frequency components when a block-based transform image

compression algorithm is applied. The high-frequency com-

ponents are quantized using larger quantizer values in such

algorithms. Hence, this region is highly distorted by image

compression algorithms. The highest distortion occurs in this

region in both DPCM-based image compression algorithms

that employ sub-sampling [2] and transform coding (DCT and



DWT) methods [15], [16]. This is due to the fact that there is

little correlation between the corner pixel values and those in

the visual region.

The drawback of the existing objective image quality as-

sessment methods for WCE is that both the PSNR and the

SSIM values decrease as a result of the artificial boundary.

This leads to unrealistic quality metrics value. Fig. 3 shows the

scaled structural similarity map of the image given in Fig. 2(a).

As we can see from the figure, the boundary between the

corner regions and the circular visual region shows a very high

difference. Unlike the rest of the region, the boundary region

pixels are uncorrelated. This leads to significantly large high-

frequency components in block-based transforms. Similarly,

the uncorrelated nature of the pixels leads to a large error in

subsampling based image compression algorithms. However,

the distortion in this region has no effect on the quality

of the visual perception of the image for of gastrointestinal

abnormalities.

Fig. 3. The structural similarity map of the image shown in Fig. 2 (a).

Similar to the corner regions, the proposed solution in

this work is the exclusion of the boundary region in the

computation of IQA metrics. In this particular case, thread-

like region whose width is 17 pixels (8 pixels into the corner

region and 8 pixels into the visual region) perpendicular to the

boundary is considered as a boundary region. The exclusion of

the boundary region pixels helps us achieve reasonable image

quality assessment metrics for WCE.

III. RESULTS AND DISCUSSIONS

The experimental setup, the results obtained from the ex-

periments, and the analysis of the results are discussed in this

section.

A. Experimental Setup

Extensive experiments were performed to compare the pro-

posed IQA methods with general-purpose IQA methods. The

experiments were designed and implemented by modifying

the existing lossy image compression algorithms, which were

proposed for WCE application. These image compression

algorithms use DCT [15], DWT [16], and DPCM with sub-

sampling [2] based image processing methods. The compar-

ative analysis of the general-purpose and the proposed IQA

methods was performed. The two selected IQA methods used

in this work for demonstration purposes are PSNR and SSIM.

These metrics are chosen here because they are widely used in

the performance analysis of image compression algorithms for

WCE. In addition to that, the two methods are good representa-

tives of the two categories of objective IQA algorithms. PSNR

is based on the statistical error, and SSIM is based on the HVS

perception [26], [27]. The main goal of these experiments is

to prove the concept in practical scenarios using comparative

analysis. It is not to compare the performance of different

image compression algorithms.

In order to evaluate the performance of the proposed IQA

methods, 120 images from Gastrolab [24] were selected. The

selected images are in RGB format with different resolutions.

The images from this database were used in the performance

evaluation of several image compression algorithms [2], [17].

B. Analysis of Results

The comparison of mean squared error (MSE) of the pixel

values in the boundary region and those inside the visual area

are shown in Fig. 4. As shown in the figure, the average MSE

values of pixels in the boundary region are 54.82, and those

of the pixel values inside the visual area are 3.55 for standard

JPEG image compression algorithm with quantizer value of

1.5. This shows that the pixels around the boundary region are

0 10 20 30 40 50 60 70 80 90 100 110 120
0

20

40

60

80

100

MSE of pixels in
the visual area

MSE of boundary pixels

Fig. 4. The mean square error value of the pixels in the boundary and
those in the visual region of 120 endoscopic images for standard JPEG image
compression algorithm with a quantizer value of 1.5.

highly affected by the compression algorithms due to the lack

of correlation of the pixel values in the two regions. This leads

to IQA metrics, which has less correlation with the subjective

image quality assessment method.

The performance of the proposed ad-hoc IQA methods

are evaluated using the implementation of eight different

image compression algorithms which were proposed for WCE

application. Both the general-purpose and the proposed IQA

methods were implemented for comparative analysis. The

summary of the comparison is given in Table I. As compared

to the conventional IQA methods, the proposed algorithm

that employs only the exclusion of corner regions in the

IQA metrics computation achieves lower values of PSNR

and SSIM. When the corner region pixels, which are not

affected by the image compression process, are excluded from

the computation of the IAQ metrics, the MSE increases as

given in equations 2 and 4. This, in turn, leads to a lower

PSNR value in all the image compression algorithms as shown

in equations 3 and 5. On the other hand, the exclusion of



0 20 40 60 80 100 120
4.2

4.4

4.6

4.8

5

5.2

5.4

5.6

5.8

ImageID

M
S

E

General−purpose MSE
MSE for WCE

(a) Mean squared-error for 120 test images [24].

0 20 40 60 80 100 120

46

46.5

47

47.5

Test image ID

P
S

N
R

 (
dB

)

General−purpose PSNR
PSNR for WCE

(b) PSNR value for 120 test images [24].

Fig. 5. Comparison of the MSE and PSNR values for the conventional and
proposed IQA methods.

TABLE I
THE COMPARISON OF DIFFERENT IMAGE QUALITY ASSESSMENT

METHODS.

Image
Compression
Methods

Conventional
IQA Methods

Proposed
IQA Methods for WCE

PSNR
(dB)

SSIM
Corner
Clipping

Corner Clipping
and Boundary
Region Exclusion

PSNR
(dB)

SSIM
PSNR
(dB)

SSIM

JPEG-based
[3]

30.79 0.9998 30.07 0.9997 36.38 0.9997

DPCM-based
[2]

38.23 0.9996 37.50 0.9995 41.65 0.9998

DCT-based
[15]

38.62 0.9987 37.89 0.9985 38.94 0.9984

DWT-based
[16]

39.90 0.9899 39.42 0.9894 40.56 0.9905

Modified
H.264 [28]

38.29 0.9990 37.57 0.9988 37.76 0.9988

DCT-based
[29]

43.60 0.9995 42.88 0.9994 43.66 0.9994

DCT-based
[30]

31.45 0.9991 30.73 0.9991 31.38 0.9995

DCT and
sub-sampling
[23]

35.99 0.9049 35.26 0.9009 36.65 0.9061

Average 37.11 0.9863 36.42 0.9857 38.37 0.9865

the boundary region which is highly affected by the image

compression processes reduces the MSE value and increases

the PSNR value. In a nutshell, the exclusion of the corner

regions alone reduces the PSNR value, whereas the removal

of corner pixels alone increases the PSNR value. For the

eight image compression algorithms implemented shown in

Table I, the exclusion of corner regions in the computation of

PSNR alone reduces it from 37.11 dB to 36.42 dB on average.

Similarly, the exclusion of both corner regions and boundary

region increases the PSNR value to 38.37 dB on average. This

gives an improvement of 1.12 dB average value of PSNR.

Hence, the combined effect is dominated by the impact of the

boundary region between the corner regions and the visual

region because this region is highly distorted by most the

image compression algorithms due to the existence of sharp

transition of pixel values at the boundary which leads to large

values of high frequency components. This is consistent in

all the images, compression algorithms. The observed PSNR

value difference obtained differs from one algorithm to another

since different algorithms induce a different level of distortion

to the boundary region of WCE image.

Unlike the PSNR, SSIM value depends on image contrast,

hue, and distribution of error [26], [27]. Because of this, we

have not obtained a consistent pattern of changes in all image

compression algorithms shown in Table I. On average, it is

observed that the SSIM decreases from 0.9863 to 0.9857 with

application of only corner clipping algorithm. It has increased

to 0.9865 of average SSIM value with both corner clipping

and boundary region omission in the computation of objective

IQA for WCE images.

Obviously, the level of distortion of the corners pixels is

zero since all the pixel values are zero. As a result, the

average SSIM and PSNR values reduce when the corner region

pixels are omitted in the computation of IQA metrics. The

overall effect of corner clipping is decreasing the considered

IQA metrics on average. The IQA metrics, which exclude

only the boundary regions in the IQA computations, achieve

a lower value of MSE compared to the original value as

depicted in Fig. 4. Hence, it results in higher values of PSNR

and SSIM than those of the conventional methods. This is

due to the fact that the boundary region is highly affected

by the image compression processes. The quantization and

sub-sampling algorithms introduce high error at the boundary

between the informative and non-informative regions due to

the abrupt transition of intensity values of pixels in WCE

images, as shown in Fig. 3. The distortion in these regions

has no significance for the detection of abnormalities in this

particular application. The modified IQA metrics, which are

implemented with a combination of both corner clipping and

boundary region exclusion in IQA metrics computation, show

more realistic metric values than the conventional methods.

Generally, the cumulative effect of excluding both regions is

dominated by the impact of the boundary pixels. This shows

that the distortion that occurs in the boundary region dominates

the overall process, and the proposed IQA metrics are higher

than that of the general-purpose IQA metrics. The simulation

result shows consistent results for the majority of the evaluated

image compression algorithms and test images, as shown in

Fig. 5 (b).

Overall, more consistent image quality assessment metrics

that are more correlated with subjective image quality as-

sessment are proposed in this work, which can be reliably

used in the image compression and super-resolution algorithm

optimization process. The exclusion of the informative regions

in the computation of the image quality assessment methods

can sightly increase the computational complexity of the

objective image quality assessment methods. However, with



the workstation computational power, we have now, its impact

in practical scenarios is negligible in terms of delay.

IV. CONCLUSION

In this paper, an application-specific objective image qual-

ity assessment method for wireless capsule endoscopy is

presented. The proposed method uses knowledge about the

statistics of the image acquisition system to improve the

performance of the general-purpose image quality assessment

methods. Specifically, the corner regions, which are not af-

fected by image compression algorithms, and the boundary re-

gions between informative and non-informative regions, which

are highly distorted by the image compression algorithms, are

excluded in the computation of the objective image quality

assessment metrics. The distortion in these two regions has

no significance for the detection of abnormalities in this

particular application. Hence, the proposed algorithm has a

higher correlation to subjective quality assessment than the

general-purpose IQA metrics. The overall impact of excluding

the two regions in the computation of IQA metrics is that

the general-purpose image quality measures are lower than

the IQA measures proposed for WCE particular application.

The proposed IQA method can be used in the optimization

of image compression and image super-resolution algorithms.

The proposed algorithm can easily be applied to other image

quality assessment methods such as FSIM [11] etc.

REFERENCES

[1] G. Iddan, G. Meron, A. Glukhovsky, and P. Swain, “Wireless capsule
endoscopy,” Nature, vol. 405, no. 6785, pp. 417–417, 2000.

[2] K. A. Fante, B. Bhaumik, and S. Chatterjee, “Design and implementation
of computationally efficient image compressor for wireless capsule
endoscopy,” Circuits, Systems, and Signal Processing, vol. 35, no. 5,
pp. 1677–1703, 2016.

[3] C. Cheng, Z. Liu, C. Hu, and M. Q.-H. Meng, “A novel wireless capsule
endoscope with jpeg compression engine,” in 2010 IEEE International
Conference on Automation and Logistics. IEEE, 2010, pp. 553–558.

[4] Y. Wang, C. Cai, and Y. Zou, “Single image super-resolution via adaptive
dictionary pair learning for wireless capsule endoscopy image,” in 2015
IEEE International Conference on Digital Signal Processing (DSP).
IEEE, 2015, pp. 595–599.

[5] P. A. Floor, I. Farup, M. Pedersen, and Ø. Hovde, “Error reduc-
tion through post processing for wireless capsule endoscope video,”
EURASIP Journal on Image and Video Processing, vol. 2020, pp. 1–15,
2020.

[6] S. Charfi and M. El Ansari, “Computer-aided diagnosis system for
colon abnormalities detection in wireless capsule endoscopy images,”
Multimedia Tools and Applications, vol. 77, no. 3, pp. 4047–4064, 2018.

[7] R. Istepanian, N. Philip, M. Martini, N. Amso, and P. Shorvon, “Sub-
jective and objective quality assessment in wireless teleultrasonography
imaging,” in 2008 30th Annual International Conference of the IEEE
Engineering in Medicine and Biology Society. IEEE, 2008, pp. 5346–
5349.

[8] Z. Wang and A. C. Bovik, “Modern image quality assessment,” Synthesis
Lectures on Image, Video, and Multimedia Processing, vol. 2, no. 1, pp.
1–156, 2006.

[9] J. Korhonen and J. You, “Peak signal-to-noise ratio revisited: Is sim-
ple beautiful?” in 2012 Fourth International Workshop on Quality of
Multimedia Experience. IEEE, 2012, pp. 37–38.

[10] Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image
quality assessment: from error visibility to structural similarity,” IEEE
transactions on image processing, vol. 13, no. 4, pp. 600–612, 2004.

[11] L. Zhang, L. Zhang, X. Mou, and D. Zhang, “Fsim: A feature similarity
index for image quality assessment,” IEEE transactions on Image
Processing, vol. 20, no. 8, pp. 2378–2386, 2011.

[12] M.-C. Lin, L.-R. Dung, and P.-K. Weng, “An ultra-low-power image
compressor for capsule endoscope,” BioMedical Engineering OnLine,
vol. 5, no. 1, pp. 1–8, 2006.

[13] P. C. Cosman, R. M. Gray, and R. A. Olshen, “Evaluating quality
of compressed medical images: Snr, subjective rating, and diagnostic
accuracy,” Proceedings of the IEEE, vol. 82, no. 6, pp. 919–932, 1994.

[14] L. He, F. Gao, W. Hou, and L. Hao, “Objective image quality as-
sessment: a survey,” International Journal of Computer Mathematics,
vol. 91, no. 11, pp. 2374–2388, 2014.

[15] P. Turcza and M. Duplaga, “Low power fpga-based image processing
core for wireless capsule endoscopy,” Sensors and Actuators A: Physical,
vol. 172, no. 2, pp. 552–560, 2011.

[16] K. A. Fante, B. Bhaumik, and S. Chatterjee, “A low-power color
mosaic image compressor based on optimal combination of 1-d discrete
wavelet packet transform and dpcm for wireless capsule endoscopy.” in
BIODEVICES, 2015, pp. 190–197.

[17] I. Intzes, H. Meng, and J. Cosmas, “An ingenious design of a high
performance-low complexity image compressor for wireless capsule
endoscopy,” Sensors, vol. 20, no. 6, p. 1617, 2020.

[18] C. Babu, D. A. Chandy, and P. Karthigaikumar, “Novel chroma sub-
sampling patterns for wireless capsule endoscopy compression,” Neural
Computing and Applications, vol. 32, no. 10, pp. 6353–6362, 2020.

[19] B. Sushma and P. Aparna, “Distributed video coding based on classi-
fication of frequency bands with block texture conditioned key frame
encoder for wireless capsule endoscopy,” Biomedical Signal Processing
and Control, vol. 60, p. 101940, 2020.

[20] M. A. Usman, M. R. Usman, and S. Y. Shin, “Quality assessment
for wireless capsule endoscopy videos compressed via hevc: From
diagnostic quality to visual perception,” Computers in biology and
medicine, vol. 91, pp. 112–134, 2017.

[21] M. A. Usman and M. G. Martini, “On the suitability of vmaf for quality
assessment of medical videos: Medical ultrasound & wireless capsule
endoscopy,” Computers in biology and medicine, vol. 113, p. 103383,
2019.

[22] L. Lévêque, H. Liu, S. Baraković, J. B. Husić, M. Martini, M. Outtas,
L. Zhang, A. Kumcu, L. Platisa, R. Rodrigues et al., “On the subjective
assessment of the perceived quality of medical images and videos,”
in 2018 Tenth International Conference on Quality of Multimedia
Experience (QoMEX). IEEE, 2018, pp. 1–6.

[23] M.-C. Lin and L.-R. Dung, “A subsample-based low-power image
compressor for capsule gastrointestinal endoscopy,” EURASIP Journal
on Advances in Signal Processing, vol. 2011, no. 1, p. 257095, 2011.

[24] “Gastrolab,” date last accessed January 10,2020. [Online]. Available:
http://gastrolab.net/

[25] Q. Huynh-Thu and M. Ghanbari, “The accuracy of psnr in predicting
video quality for different video scenes and frame rates,” Telecommuni-
cation Systems, vol. 49, no. 1, pp. 35–48, 2012.

[26] A. Hore and D. Ziou, “Image quality metrics: Psnr vs. ssim,” in 2010
20th international conference on pattern recognition. IEEE, 2010, pp.
2366–2369.

[27] Z. Kotevski and P. Mitrevski, “Experimental comparison of psnr and
ssim metrics for video quality estimation,” in International Conference
on ICT Innovations. Springer, 2009, pp. 357–366.

[28] L.-R. Dung, Y.-Y. Wu, H.-C. Lai, and P.-K. Weng, “A modified h.
264 intra-frame video encoder for capsule endoscope,” in 2008 IEEE
Biomedical Circuits and Systems Conference. IEEE, 2008, pp. 61–64.

[29] P. Turcza and M. Duplaga, “Hardware-efficient low-power image pro-
cessing system for wireless capsule endoscopy,” IEEE journal of
biomedical and health informatics, vol. 17, no. 6, pp. 1046–1056, 2013.

[30] K. Wahid, S.-B. Ko, and D. Teng, “Efficient hardware implementation
of an image compressor for wireless capsule endoscopy applications,” in
2008 IEEE International Joint Conference on Neural Networks (IEEE
World Congress on Computational Intelligence). IEEE, 2008, pp. 2761–
2765.



Kinde A. Fante received the BSc degree in Electri-
cal Engineering from Bahir Dar University in 2008,
the M.Tech degree in Electronics and Computer
Engineering from Addis Ababa University, Ethiopia,
in 2010,Ethiopia and the Ph.D. degree in Electrical
Engineering from the Indian Institute of Technology
Delhi, India, in 2016. Currently, he is an Assistant
Professor in the Faculty of Electrical and Computer
Engineering, Jimma Institute of Technology, Jimma
University. His research interests include Biomedi-
cal Signal processing, low-power integrated circuits,

VLSI design for real-time image and video processing, and machine learning.

Fetulhak Abdurahman received the BSc in Elec-
trical Engineering in 2009 and the MSc in Computer
Engineering in 2014 from Addis Ababa University.
From 2009- 2020 working as a lecturer in Jimma
University, Ethiopia. His research interest includes
computer vision, pattern recognition and natural
language processing.

Mulugeta T. Gemeda received a BSc degree in
Electrical and Computer Engineering in 2017 and
M.Sc. degree in Electronics Communication Engi-
neering from Jimma University, Ethiopia, in 2019.
Currently, he is working as Lecturer at Jimma In-
stitute of Technology in the Faculty of Electrical
and Computer Engineering, Jimma University. His
research interests include antenna design, mobile
communication, and computer networking.