DWT based Low Power Image Compressor for Wireless Capsule

Endoscopy

Kushaagra Goyal, Abhishek Lal and Basabi Bhaumik

Department of Electrical Engineering, Indian Institute of Technology Delhi, Hauz Khas, New Delhi 110016, India

Keywords:

Wireless Capsule Endoscopy, Low Power, Discrete Wavelet Transform, Serialiser.

Abstract:

In WCE literature so far, the stress is on having an image compressor with low power consumption and silicon

area. However one needs to consider the image compressor along with the serialiser, the interface between

image compressor and transmitter as a single unit. In this paper, we propose the design of a hardware efﬁcient,

low power image compression system along with the serialiser for wireless capsule endoscopy. It is based on

integer version of discrete wavelet transform and uses low complexity encoders like adaptive Golomb-Rice

encoder. An alternative architecture for serialiser is proposed speciﬁc to the algorithm which runs at only 8

times instead of 32 times the frequency required at the existing compressors in the literature. The proposed

algorithm gives a compression of 91.88 percent at a PSNR of 38.17. The implementation of the compressor

plus serialiser in 130nm HS (high speed) standard CMOS process technology consumes 16.9uW of power at

2 frames per second for 256×256 image. Compared to the existing designs at similar power consumption,

the proposed scheme reduces the serialiser’s frequency by a factor of four besides giving at least 1.5 % higher

compression.

1 INTRODUCTION

Wireless Capsule endoscopy uses a miniature camera

to capture images of the gastrointestinal tract. The

whole system is built into a small capsule for min-

imum invasiveness. The patient ingests a small vi-

tamin size capsule containing CMOS image sensor

array, LEDs, battery, and an RF transmitter. It cap-

tures images and sends them to an outside workstation

where the images can be analysed by gastroentolo-

gists. The battery for the capsules runs for about 8-10

hours. A major research thrust is on incorporating ad-

ditional functionalities like drug delivery and locomo-

tion on to the chip (Bruaene et al., 2015; Koulaouzidis

and Iakovidis, 2015; Hale et al., 2014). Introduc-

ing these new features reduces the capsule area de-

voted to the power source, thereby necessitating a

decrease in power consumption to lengthen the cap-

sule lifetime. In this paper we focus on designing

a low power image compressor along with its inter-

face to the transmitter. Our scheme employs hard-

ware efﬁcient and low complexity methods like inte-

ger discrete wavelet transform (Bhanu and Chilam-

buchelvan, 2012) and adaptive Golomb-Rice encoder

(Memon, 1998). We also propose a new architecture

for the serialiser which works at 8 times the frequency

of the compressor.

The prior works which are based on DCT (Dis-

crete Cosine Transform) divide the image into non

overlapping blocks and employ transforms to reduce

2D spatial redundancy in an image. Since the im-

age comes in raster-scan fashion, the above technique

necessitates the use of memory for storing few rows

of incoming image. The computational resources re-

quired to compute these transforms are also high. As

a result they are not hardware efﬁcient and consume

lot of power. A low memory DPCM (Differential

Pulse Code Modulation) based design was proposed

in (Khan and Wahid, 2013; Khan and Wahid, 2011a).

It used hardware efﬁcient techniques at the cost of

low compression rates. Recently, in (Fante et al.,

2016), an image compression scheme was proposed

which was based on optimal combination of quantisa-

tion and subsampling thereby achieving higher com-

pression rate as compared to the previously proposed

schemes (Khan and Wahid, 2013; Khan and Wahid,

2011a). However, the serialiser, which runs at 32

times the compressor frequency is the major power

consumer and the existing designs did not optimise

its power consumption.

In this paper, we treat compressor and serialiser

as a unit and propose a new architecture for the

Goyal K., Lal A. and Bhaumik B.

DWT based Low Power Image Compressor for Wireless Capsule Endoscopy.

DOI: 10.5220/0006103000170024

In Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2017), pages 17-24

ISBN: 978-989-758-216-5

serialiser which works at 8 times the frequency of

the compressor. Furthermore, we employ 1D DWT

(Discrete Wavelet Transform) instead of subsampling

for achieving higher compression while maintaining

good image quality. Although using 2D DWT would

have increased the compression, it would have sub-

stantially increased the hardware requirements. Using

1D DWT keeps the hardware minimal and increases

the compression with good image quality.

This paper is organised as follows – a detailed dis-

cussion of the proposed algorithm is given in section

2, performance results are described in section 3, sec-

tion 4 gives details about the hardware realisation and

the power consumption. Finally in section 5 we con-

clude this paper.

2 PROPOSED ALGORITHM

The algorithms proposed by (Khan and Wahid, 2013)

and (Fante et al., 2016) are power efﬁcient as they

minimize the usage of buffer memory while achiev-

ing a very high compression ratio. In this section

we will discuss the techniques employed to achieve

a better compression ratio along with reduced power

consumption. The compression algorithm used takes

hardware feasibility into account. We have ensured

that the average and minimum PSNR (Peak signal-to-

noise ratio) is over the recommended value (Cosman

et al., 1994; Philip et al., 2008). The algorithm is

designed such that the transmitter has to run at a fre-

quency which is only at 8 times higher than that of

the compressor. Our design reduces the serialiser fre-

quency by a factor of 4 as compared to the existing

designs((Khan and Wahid, 2011b) and (Fante et al.,

2016)). The block diagram of the image compressor

and decompressor is shown in the Figure 1 and 2 re-

spectively.

Figure 1: Block Diagram of Image Compressor.

2.1 Forward RCT Transform

RCT stands for reversible colour transform. The im-

age obtained from the camera is of RGB888 format.

Figure 2: Block Diagram of Image Decompressor.

It is ﬁrst converted from RGB color space to YUV

color space using the following equations:

Y =



R + 2G + B



,U = R − G,V = G − B (1)

Figure 3 shows the R, G and B values of the pixels

along the middle row of an image. We see that the

Red, Green and Blue channels are highly correlated.

We also observe that red is the most dominant color as

compared to green and blue. Thus we can safely as-

sume that the signal which is the difference between

Green and Blue will contain very less information.

Therefore, we transformed the color space from RGB

to YUV to de-correlate the image (Fante et al., 2016).

Figure 3: R, G and B value of pixel along middle row.

Table 1 and 2 shows the correlation between dif-

ferent color channels. We can easily see that the cor-

relation in the transformed color space is reduced.

Table 1: Correlation between RGB components.

Correlation Matrix Red Green Blue

Red 1 0.9626 0.9159

Green 0.9626 1 0.9864

Blue 0.9159 0.9864 1

The Y component corresponds to intensity value

while U and V represents the chrominance compo-

nents. It should be noted that the value of Y varies

BIODEVICES 2017 - 10th International Conference on Biomedical Electronics and Devices

Table 2: Correlation between YUV components.

Correlation Matrix Y U V

Y 1 0.6814 0.6599

U 0.6814 1 0.8554

V 0.6599 0.8544 1

from 0 to 255 whereas the values of U and V varies

from -255 to 255.

2.2 Quantisation

The output of RCT forward transform is quantized

ﬁrst – in order to implement a hardware efﬁcient

quantization, we ignore the least signiﬁcant bits. The

quantization formula used is:



P + Q/2



(2)

Where P is the value of the pixel, Q is the quantisa-

tion value given by 2

where l is the number of bits

to be quantised and P

is the quantised pixel value.

In our design we quantized the YUV image by 3 bits.

Increasing it to 4 bits resulted in unacceptable PSNR

(Peak Signal to Noise Ratio) values while 2-bits quan-

tization reduced the compression ratio. Therefore 3

bits was the sweet spot in our design as shown in Sec-

tion 3.Quantization also reduces the standard devia-

tion of the data which will further helps us in adaptive

Golomb-Rice encoding.

2.3 DWT

The algorithm proposed by (Fante et al., 2016) sub-

samples the U and V component by 4 since U and

V contains less information compared to Y. In our

implementation,we have used the 5-3 integer DWT

to down-sample the data (Jing et al., 2008). The U

and V components are successively passed through

two DWT blocks while retaining the lower frequency

components at each stage. Since images of interest

are sparse at higher frequencies, our method achieves

compression comparable to the previously employed

subsampling approach while retaining a better image

quality. For hardware efﬁciency we implemented the

lifting architecture of 5-3 integer DWT (Bhanu and

Chilambuchelvan, 2012).

2.4 DPCM

DPCM exploits the spatial redundancy of an image.

It uses a prediction function which predicts the value

of a pixel given the previous values. In our design we

use the simplest prediction function i.e. the predicted

value is same as the previous value. Better results

could have been achieved by using better prediction

functions or using 2D prediction scheme at the cost

of higher hardware requirements.

2.5 Corner Clipper

While the lens of the capsule generates a circular im-

age, the image sensor is rectangular. Due to this all

pixels outside the circular region have a value of zero.

The Golomb Rice encoder would encode these pixels

by using a single bit for each component. As proposed

by (Khan and Wahid, 2011a), we use linear cropping

to crop these pixels. This process is hardware efﬁcient

and increases the Compression Rate.

2.6 Golomb Encoder

Golomb Rice encoder was shown to be an hardware

efﬁcient entropy encoder for endoscopy images. In

(Khan and Wahid, 2011a), Golomb Rice parameter

m = 2

was static but it gave poor compression. In

(Fante et al., 2016), this parameter changed on the

ﬂy and was determined using a single context to re-

duce computational complexity. Using such an adap-

tive Golomb-Rice encoder improved the compression

rate. Both of these encoders produced maximum code

length of 32. This required the serialiser to work at 32

times the frequency which consumed a lot of power.

We investigated the effect of reducing the max code

length to 16 bit by limiting the parameter glimit to

16 in the Golomb Rice encoder. Doing this resulted

in the decrease in the Compression Rate(CR). But by

replacing subsampling by DWT in our algorithm, we

were able to achieve an extra bit of quantisation. This

helped in decreasing the max code length to 16 with-

out seriously affecting the Compression Rate.

Table 3 summarises the details of our encoders

in comparison to the ones proposed by (Khan and

Wahid, 2013) and (Fante et al., 2016).

3 PERFORMANCE EVALUATION

For evaluation purposes, 120 images were collected

from Gastrolab (Gastrolab, 2014). It includes the en-

tire GI tract and thus is a good representative for the

entire digestive system. The performance of the pro-

posed compression algorithm is evaluated using Com-

pression Rate(CR) which is given by:

CR =



1 −

Image Size After Compression

Image Size Before Compression



× 100

(3)

DWT based Low Power Image Compressor for Wireless Capsule Endoscopy

Table 3: Golomb Encoding Comparison Table.

Aspect Golomb (Proposed) Golomb (Fante) Golomb (Khan)

Parameter k Dynamically calulated Dynamically Calculated static

Max Code Length 16 32 32

Compression Medium Compression Medium Compression Low Compression

Complexity Medium Complexity Medium Complexity Low Complexity

Table 4: Performance of Algorithm for Different Parameters.

Quantisation Golomb-Encoder CR PSNR

Q = 1 Adaptive glimit=16 80.06 46.56

Q = 2 Adaptive glimit=16 84.99 44.49

Q = 4 Adaptive glimit=16 89.22 42.27

Q = 8 (Proposed) Adaptive glimit=16 91.88 38.17

Q = 8 Adaptive glimit=32 91.95 38.17

Q = 16 Adaptive glimit=16 93.38 32.87

The proposed image compression algorithm is

lossy due to quantisation and DWT where high fre-

quency components are dropped. The quality of the

reconstructed image is measured using PSNR which

is given by (Korhonen and Junyong, 2012) :

PSNR = 10 × log



MAX

MSE



(4a)

MSE =

3HW

k=3

∑

k=1

i=H

∑

i=1

j=W

∑

j=1

(I(i, j,k) − K(i, j,k))

(4b)

MAX is the maximum possible pixel value which is

255 in our case, as we are using 8 bits per sample. H

and W are the height and width of the original image I

and K represents the reconstructed noisy image. The

algorithm is tested for different values of quantisation

and different setting of Golomb encoder. The perfor-

mance under different settings is tabulated in Table 4.

From the Table 4, we observe that on increasing

quantisation, the compression rate increases but the

quality of the reconstructed image falls. An optimum

tradeoff between the achieved compression and the

image quality is achieved at Q = 8. Setting the maxi-

mum code length to 16 instead of 32 in Golomb Rice

encoder decreases the compression by only 0.07 per-

cent but decreases the power in the serialiser and al-

lows us to design an efﬁcient architecture of the seri-

aliser which can run at 8 times the frequency of the

compressor. Thus in our proposed algorithm, we use

adaptive Golomb Rice encoder with max code length

set to 16 and are able to achieve compression of 91.88

percent at a PSNR of 38.17. In Table 5 we com-

pare the performance of our algorithm with the recent

existing works. Our implementation achieves higher

compression rate than the previous implementations.

Figure 4 shows two images compressed and recon-

structed using our algorithm.It can be seen that the

Figure 4: Original (top) and Reconstructed(bottom) images

with PSNR and CR value for 2 out of the 120 images.

reconstructed image quality is very good and it is al-

most indistinguishable from the original to the human

eye.

4 HARDWARE REALISATION

4.1 Image Compressor

The block diagram of the proposed architecture for

the image compressor is shown in Figure 5. The im-

age compressor has three inputs : 8 bit Pixel [7:0], Re-

set, and CLK. Its output includes a 16 bit code word,

code word length and a valid bit. The image com-

pressor accepts the input in RGB format and is used

to compress a 256*256 image with 24 bits per pixel

at 2 frames per second.

The control unit is the heart of the design and gen-

erates the control signals for the entire design. It takes

BIODEVICES 2017 - 10th International Conference on Biomedical Electronics and Devices

Table 5: Comparison with Previous Works. In this table, Q stands for quantisation, SS stands for sub-sampling , GR stands

for Golomb-Rice, RL stands for Run Length.

Method Colour

Space

Entropy

Encoder

CR PSNR

DPCM + Q + SS (Fante

et al., 2016)

YUV Adaptive GR

with code limit

= 32

90.35 40.66

DPCM (Khan and

Wahid, 2011a)

YUV Static GR 76.64 52.60

DPCM + SS (Khan and

Wahid, 2011b)

YUV 812 Static GR 82.57 41.01

DPCM + SS (Khan and

Wahid, 2012)

YUV 16:1:1 Static GR,

code limit=32

85.72 38.37

DCT (Turcza and

Duplaga, 2011)

Huffman 91.34 36.67

DCT (Turcza and

Duplaga, 2013)

GR with RL 91.35 38.59

DPCM + Q + DWT (This

Work)

YUV Adaptive GR

with code limit

= 16

91.88 38.17

Figure 5: Hardware Architecture of Image Compressor.

CLK and reset as input and generates two separate

clocks for the compressor and serialiser. Moreover,

it contains a column and a row counter which keeps

track of the current pixel being processed and also

helps in corner clipping. It further generates the “valid

signal” which signiﬁes whether the current output is

valid or not. The pixels is encoded using quantisation,

DWT transform and Golomb encoding.

The forward RCT module is a purely combina-

tional block and performs colour space transforma-

tion from RGB to YUV space. The quantiser per-

forms the 3 bit quantisation on the output of the RCT

module. 2 level 1D DWT is used on the quantized U

and V values. Lifting architecture is used to imple-

ment the 5-3 integer wavelet. The output is encoded

using simple DPCM and passed through Golomb en-

coder. The Golomb encoder involved mapping neg-

ative values to positive values, ﬁnding parameter k

DWT based Low Power Image Compressor for Wireless Capsule Endoscopy

using context variables and encoding the given pixel.

It produces a 16 bit code word along with the code

word length which is fed to the serialiser. The entire

design is hardware efﬁcient and uses simple computa-

tions along with no buffer memory. Small sets of reg-

isters are used in RGB, DWT , DPCM and Golomb

encoder. In total, 229 registers are used in the design

of the compressor. The image compressor processes

each pixel in 3 cycles and thus for a 256*256 image

at 2fps requires a clock frequency of 393.216 KHz.

4.2 Serialiser

The Serialiser proposed by (Fante et al., 2016) and

(Khan and Wahid, 2013) works at 32 times the fre-

quency of the compressor. It was also mentioned that

the serialiser was a major power hog. In order to re-

duce the consumption of power, ﬁrst of all we limited

the input to the serialiser to 16 bit code by changing

the Golomb encoder in the compressor. Secondly, in

our algorithm we were effectively sub-sampling the

U and V components by 4. This implied that for ev-

ery 12 clock cycles of the compressor, we sent 4 Y

values, 1 U value and 1 V value, whereas in 6 clock

cycles no data was produced by the compressor. The

pattern of the data produced by the compressor is like

: YUVY- -Y- -Y- -, where ‘-’ represents no data. If

we consider the worst case scenario, where all these

data produced are of 16 bits, then minimum frequency

of data transmission required, R, can be calculated as

12*R = 16*6 , which gives R =8. Thus from this cal-

culation, we can observe that we can run the serialiser

at 8 times the clock frequency of the compressor.

It must be noted that compressor can output up to

16 bits in 1 compressor clock cycle but the serialiser

can send at most 8 bits per compressor clock cycle.

Therefore, it is pretty obvious that we need a FIFO to

solve our problem. In order to calculate the maximum

buffer sizer to prevent overﬂow we again consider the

worst possible scenario by assuming the output of ev-

ery channel to be 16 bits. In order to prevent overﬂow

we must ensure that after every 12th clock cycle the

buffer occupancy is zero. It could be easily seen that

for the given pattern of YUV the maximum buffer oc-

cupancy that could ever be reached is 40. Therefore

by running the FIFO at 8 times the frequency of the

compressor and keeping its size at 40 works perfectly

ﬁne as a serialiser for our algorithm.

The architecture of the proposed serialiser is given

in Figure 6.

We can also adapt the original serialiser architec-

ture proposed in (Khan and Wahid, 2013) for 16 bit

Golomb encoder. The power consumption will reduce

by more than half since the number of registers will be

Table 6: Power Consumption of Proposed Compressor with

the New Serialiser.

Supply Voltage 1.2 Volts

Compressor Power 10.7 uW

Serialiser Power 6.2 uW

Total Power 16.9 uW

Dynamic Power 9.71 uW

Leakage Power 7.21 uW

Table 7: Power Consumption of Proposed Compressor with

the Old Serialiser but operating with 16 bit input.

Supply Voltage 1.2 Volts

Compressor Power 11.64 uW

Serialiser Power 2.56 uW

Total Power 14.2 uW

Dynamic Power 7.60 uW

Leakage Power 6.60 uW

halved and the frequency of operation is also halved.

On comparing the power consumption between pro-

posed serialiser and the original archtecture for 16

bit, we ﬁnd that original architecture requires lower

power. The reason is straightforward, since our archi-

tecture has 40 registers operating at 8 times clock fre-

quency, whereas the original architecture has 16 reg-

isters operating at clock frequency. Moreover we have

a barrel shifter which consumes majority of the com-

binational power. But the advantage of our serialiser

architecture is that the overall frequency of operation

is halved. Note that both the serialisers with 16 bit in-

put consume less power as compared to the previous

designs (Fante et al., 2016; Khan and Wahid, 2013)

which work at 32 times the operating frequency of

compressor.

4.3 Power Comparison

The proposed image compressor and the serialiser

was implemented in Verilog. It was synthesized using

Synopsys Design vision and mapped to UMC 130nm

CMOS process using High Speed Faraday standard

cell libraries. The whole image compressor along

with the new proposed serialiser takes 1463 cells.

The image compressor with the serialiser proposed

in (Fante et al., 2016) adapted for 16 bit takes 1230

cells.The power consumption of the two designs, one

with the new proposed serialiser and other one with

the serialiser proposed by (Fante et al., 2016) adapted

for 16 bit encoder are given in Tables 6 and 7. The

layout of the proposed compressor is shown in Figure

The proposed scheme is hardware efﬁcient since

the entire design involves simple operations like ad-

dition, and shifting and expensive operations like di-

BIODEVICES 2017 - 10th International Conference on Biomedical Electronics and Devices

Figure 6: Hardware Architecture of Serialiser.

Table 8: Power Comparison with Previous Works.

Existing Works Process

Technology

Chip Area

(mm

)

Gate

Count

Power

Consump-

tion

(mW)

Frame Rate

(fps) (image

size)

ASIC(Khan and

Wahid, 2011a)

0.18 um 0.0256 2k 0.018 2(3*320*240)

ASIC(Khan and

Wahid, 2012)

0.18 um 0.0320 2k 0.042 2(3*256*256)

ASIC(Khan and

Wahid, 2011b)

0.18 um 0.0256 2k 0.042 2(3*256*256)

ASIC (Fante et al.,

2016)

0.13 um 0.018 2.2k 0.035 2(256*256*3)

ASIC (Lin and Weng,

2006)

0.18 um 0.329 31k 14.92 2(512*512*3)

Simulation (This

Work : with New

Serialiser)

0.13 um 0.021 5.5k 0.0169 2(256*256*3)

Simulation (This

Work : with 16 bit Old

Serialiser)

0.13 um 0.019 4.9k 0.0142 2(256*256*3)

vision and multiplication are avoided. The order of

computational complexity of our algorithm is O(n),

which is similar to the design proposed in (Fante et al.,

2016). As compared to their design, we required

some more registers in the DWT block, but overall

we are increasing the compression at a minimal in-

crease in hardware. By limiting the max length of

code to 16 in the Golomb Rice encoder, we simpliﬁed

the design of the serialiser. If we consider the com-

pressor along with the serialiser as a unit, then we

obtain higher compression as compared to previous

designs at almost similar hardware complexity and

power consumption. Our implementation requires no

memory buffer and does not require complex compu-

tations.Table 8 compares the power consumption of

our design with the previous works.

5 CONCLUSIONS

In this paper, we presented a hardware efﬁcient image

compressor along with the serialiser for application in

DWT based Low Power Image Compressor for Wireless Capsule Endoscopy

Figure 7: Layout of the Proposed Image Compressor.

wireless capsule endoscopy. It was based on compu-

tationally simple techniques like 1D integer wavelet

transform, DPCM, color transformation and Golomb

Rice encoder. The performance of the algorithm was

evaluated on the basis of PSNR and Compression Ra-

tio. Our image compressor was able to achieve a com-

pression of 91.88 percent at a PSNR 38.17. An alter-

native architecture for the serialiser was also proposed

speciﬁc to the implemented algorithm which ran at

only 8 times the frequency of the compressor. The

hardware implementation of the proposed compres-

sor along with two different serialisers using Faraday

HS library standard cells in UMC130nm process con-

sumes 14.2uW and 16.9 uW respectively. The archi-

tecture is designed for a 256*256 image at 2 frames

per second. As compared to the existing DCT based

implementations, we get as good a compression ratio

but with very low power consumption. In comparison

to the DPCM based approaches, our algorithm gives

higher compression with similar power consumption.

Moreover, we were able to optimize the design of the

serialiser so that it works at lower frequency. We be-

lieve that the proposed image compressor along with

the serialiser is a good candidate for WCE applica-

tions as it has a high compression ratio, good recon-

structed image quality, low power consumption, and

small area.

REFERENCES

Bhanu, U. and Chilambuchelvan, D. A. (2012). A de-

tailed suvey on vlsi architectures for lifting based dwt

for efﬁcient hardware implementation. International

Journal of VLSI design and Communication Systems,

pages 207–214.

Bruaene, C. V. D., Looze, D. D., and Hindryckx, P. (2015).

Small bowel capsule endoscopy: Where are we after

15 years of use? World Journal of Gastrointestinal

Endoscopy, pages 13–36.

Cosman, P. C., Gray, R. M., and Olshen, R. A. (1994). Eval-

uating quality of compressed medical images: Snr,

subjective rating, and diagnostic accuracy. In Pro-

ceedings of the IEEE, volume 82, pages 919–932.

Fante, K. A., Bhaumik, B., and Chatterjee, S. (2016). De-

sign and implementation of computationally efﬁcient

image compressor for wireless capsule endoscopy.

Circuits Systems and Signal Processing, 35:1677–

1703.

Gastrolab (2014). http://www.gastrolab.net.

Hale, M. F., Sidhu, R., and McAlindon, M. E. (2014).

Capsule endoscopy: Current practice and future di-

rections. World Journal of Gastroenterology, pages

7752–7759.

Jing, Z., Jin-yun, F., and Cheng-de, H. (2008). The selection

of reversible integer-to-integer wavelet transforms for

dem multi-scale representation and progressive com-

pression. International Archives of Photogramme-

try, Remote Sensing and Spatial Information Science,

pages 1010–1024.

Khan, T. H. and Wahid, K. (2011a). Lossless and low power

image compressor for wireless capsule endoscopy.

VLSI Design.

Khan, T. H. and Wahid, K. (2011b). Low power and low

complexity compressor for video capsule endoscopy.

In IEEE Transactions on Circuits And Systems For

Video Technology, volume 21, page 15341546.

Khan, T. H. and Wahid, K. (2012). Implantable narrow band

image compressor for capsule endoscopy. In IEEE In-

ternational Symposium on Circuits and Systems (IS-

CAS), Seoul, South Korea, page 22032206.

Khan, T. H. and Wahid, K. (2013). Subsample based image

compression for capsule endoscopy. Real-Time Image

Processing, pages 5–19.

Korhonen, J. and Junyong, Y. (2012). Peak signal to noise

ratio revisited: Is simple beautiful? Fourth Interna-

tional Workshop on Quality of Multimedia Experience

(QoMEX), Yarra Valley, VIC, Australia, pages 37–38.

Koulaouzidis, A. and Iakovidis, D. K. (2015). Wireless en-

doscopy in 2020: Will it still be a capsule? World

Journal of Gastroenterology, pages 5119–5130.

Lin, M.-Ch, D. L.-R. and Weng, P, K. (2006). An ultra-

low-power image compressor for capsule endoscope.

Biomedical Engineering Online, pages 1–8.

Memon, N. (1998). Adaptive coding of dct coefﬁcients by

golomb-rice codes. In Proceedings of International

Conference on Image Processing.

Philip, N., Martini, M. G., and Amso, N. (2008). Subjec-

tive and objective quality assessment in wireless tele

ultrasonography imaging. In Proceedings of the In-

ternational Conference of the IEEE Engineering in

Medicine and Biology Society, pages 5346–5349.

Turcza, P. and Duplaga, M. (2011). Low power fpga-based

image processing core for wireless capsule endoscopy.

Sensors and Actuators A: Physical, pages 552–560.

Turcza, P. and Duplaga, M. (2013). Hardware-efﬁcient low-

power image processing system for wireless capsule

endoscopy. IEEE Journal Of Biomedical And Health

Informatics, pages 1046–1056.

BIODEVICES 2017 - 10th International Conference on Biomedical Electronics and Devices