Kinde A. Fante, B. Bhaumik & S. Chatterjee

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chrominance component sub-sampling, differential pulse code modulation and Golomb-Rice entropy encoder. All the modules are highly optimized.
A Low-Power Color Mosaic Image Compressor for Wireless Capsule Endoscopy Kinde A. Fante, B. Bhaumik & S. Chatterjee Department of Electrical Engineering, Indian Institute of Technology Delhi, New Delhi-110016, India E-mail: Abstract Image compression algorithm inside wireless capsule endoscopy (WCE) should have low power consumption, small silicon area, high compression efficiency, and high reconstructed image quality. A lowcomplexity image compression algorithm which meets these contradicting and stringent constraints is presented in this work. By utilizing unique properties of human gastrointestinal tract images, computationally simple methods are employed. The employed methods are lifting scheme based two level 1-D discrete wavelet packet transform, uniform quantization, chrominance component sub-sampling, differential pulse code modulation and Golomb-Rice entropy encoder. All the modules are highly optimized from computational complexity, efficiency and memory requirement perspectives. The proposed algorithm requires neither demosaicking nor deinterleaving operations that require large memory and consume a significant amount of power. It achieves a compression rate of 81.31 % with peak signal to noise ratio (PSNR) of 39.45 dB. The implementation of the algorithm in 130 nm standard CMOS process technology occupies a core area of 0.342 mm×0.342 mm. It consumes 48.4 µW of power for encoding two 512×512 frames per second.

Table 1: The sampling pattern of: (left) Bayer mosaic data, and (right) 1D wavelet transform.

G0,0 R0,1 G0,2 R0,3 G0,4 R0,5 B1,0 G1,1 B1,2 G1,3 B1,4 G1,5

RF transmitter

Offline link +

Data recorder

Computer

Image compressor

_

CMOS image sensor

G2i,2 j + G2i,2 j+2 1 Row: HR[2i, 2 j + 1] = R2i,2 j+1 − b c ≈ R−G (3) 2 R+G HR[2i, 2 j − 1] + HR[2i, 2 j + 1] + 2 c≈ (4) LG[2i, 2 j] = G2i,2 j + b 4 2 B2i+1,2 j + B2i+1,2 j+2 nd 2 Row: HG[2i + 1, 2 j + 1] = G2i+1,2 j+1 − b c ≈ G − B (5) 2 HG[2i + 1, 2 j − 1] + HG[2i + 1, 2 j + 1] + 2 G+B LB[2i + 1, 2 j] = B2i+1,2 j + b c≈ 4 2 (6) st

• HR, HG are low-pass signals and LG, LB are high-pass signals (see Figure 4). • Endoscopic color images: R component has the highest freqeuncy content and B component has the lowest frequency content. • Low-pass filtering operations, (4) and (6), are modified as: LG[2i, 2 j] = G2i,2 j (7) LB[2i + 1, 2 j] = B2i+1,2 j (8)

L e n s

LEDs

Battery

L0,0 H0,1 L0,2 H0,3 L0,4 H0,5 L1,0 H1,1 L1,2 H1,3 L1,4 H1,5

By applying (1) and (2) on Bayer mosaic data of Table 1:

INTRODUCTION Real-time link

[email protected]

Wireless capsule endoscopy

Figure 1: Endoscopy system

Main feature of WCE: • Can reach small intestine (dead zone for wired endoscopy). • Non-invasive and easy procedure.

Objective of this work: to increase WCE operation time by incorporating computationally simple and efficient image compressor with minimal overhead.

1,000 750 500 250 0

4000 2000 0

−80 −40 0 40 80 (a) Histogram of HH band.

4000

1. Analysis of Color Mosaic Image Using Wavelet

800 600 400 200 0

0

−80

−40

0

40

80

(b) Histogram of HL band.

row counter

8-bit binary encoder Modified 1D DWPT

Uniform quantizer

sub-sampling counter Code validate

Corner clipper

Sub-sampler

1D DWPT

AGR encoder

DPCM

VC

CW(0:31) M u CWL(0:4) x

Table 3: The comparison of the compressor with other works. 0 50 100 1250 200 (c) Histogram of LH band.

Methods Compression Rate (%) Overall PSNR (dB)

0 50 100 150 200 (d) Histogram of LL band.

Technology

Figure 6: Histogram of the subbands of the 2nd level wavelet packet decomposition of mosaic image shown in Figure 4.

Figure 2: The Bayer color filters array (CFA) (a), mosaic WCE image (b) and typical WCE full color image (taken from Gastrolab).

2. Differential Pulse Code Modulation (DPCM): Prediction error is: dXr,c = Xr,c − Xr,c−1. where r, c are row and column indexes. 4000

• Mosaic image: has high frequency content due to the interleaved color bands. • The conventional image compression algorithms are inefficient for encoding mosaic image.

• Silicon area and power consumption overhead reduction: lifting scheme based 1-D DWPT using Le Gall (5,3) spline filter. Level 1

Level2 LL HL LH HH

H

Figure 3: Digrammatic representation of 1-D dyadic decompostion for two decomposition levels (using horizontal fitering).

2500 2000

3000

HL

LH

1000 0

−80

−40

0 (a)

40

0

80

−80

−40

0 (b)

40

Figure 7: Histogram of the DPCM prediction error of LH (a) and LL (b) subbands of image shown in Figure 4.

The proposed image compressor consists of: • Image prep-processing: corner clipping, sub-sampling, uniform quantization. – Corner clipper: Corner pixels has no important information. – Uniform quantization: to reduce high frequency content. – Sub-sampling: Only H-subband is downsammpled which is low-pass signal for WCE image.

46.8

36.2

35.7

39.5

ASIC 0.18 µm

ASIC 0.18 µm

FPGA 65nm

ASIC 0.13µm

90K gates

318K

-

117K

93.81K

YES

10.5K

0

1.55 (0.28 fps)

9.17 (2 fps)

7 (7 fps)

0.048 (2 fps)

1.8

1.8

1.2

1.2

Uniform quantizer

Level 1 1-D DWPT

Subsampling

Level 2 1-D DWPT

DPCM

• The silicon chip area and power consumption of the proposed image compressor is significantly lower than previous works. The future work includes: • Design of capsule endoscopic system which uses the proposed image compressor and test its performance in real world. • The study of impact of the distortion introduced due to subsampling and uniform quantization on automatic abnormality algorithms.

[Idden et al., 2000] Idden, G., Meron, G., Glukhovsky, A., and Swain, P. (2000). Wireless capsule endoscopy. Nature.

bitstream

[Lin and Dung, 2011] Lin, M. and Dung, L. (2011). A subsample-based low-power image compressor for capsule gastrointestinal endoscopy. EURASIP Journal on Advances in Signal Processing, 2011.

Output Bitstream

• It uses 1D operations which does not require large memory buffer.

01010101010 AGR encoder

Figure 8: Block diagram of the proposed image compressor 001100010

• The proposed image compressor gives high compression rate based on optimal combination of computationally simple methods.

References Corner clipper

Figure 4: Two level 1D wavelet packet decomposition of mosaic image shown in Figure 2 (b) shown as greylevel image.

AGR decoder

inverse DPCM

Level 2 1-D IDWPT

Upsampling

Level 1 1-D IDWPT

Uniform dequantizer

Corner filler

(1) (2)

81.31

80

Mosaic image

X[2n] + X[2n + 2] H[2n + 1] = X[2n + 1] − b c 2 H[2n − 1] + H[2n + 1] + 2 L[2n] = X[2n] + b c 4

91.2

Conclusion and Future Works

500

HH

• 1D wavelet decomposition of input sample sequence X[n] into high frequency component, H[n], and low frequency component, L[n] :

82.0

1000

• Entropy Encoder: Locally adaptive Golomb-Rice Encoder (AGR), since the source has laplacian-like distribution. LL

72.7

1500

• Redundancy removal: DWPT ( both spatial and spectral) and DPCM (spatial).

H

Core size (µm2) Memory (byte) Power consumption (mW) Supply Voltage (V)

(Xie et al.,2006) (Lin et al.,2011) (Turcza et al.,2013) This work JPEG-LS DCT DCT DWPT

2000

• Discrete wavelet packet transform (DWPT): remove the spatial and spectral redundancies in mosaic images simultaneously.

L

D IN(0:7) e m u x

column counter

Figure 11: Layout view of the core of the image compressor implementation.

2000

Mosaic image

first_pixel_of_row

• The modified wavelet analysis gives a better compression rate. • No significant statistical properties reduction after 2nd level DWPT. • The decomposition level is fixed to two.

Desirable Properties of the methods: • Require no (small) memory buffer and computationally simple. • Remove the spatial and spectral redundancy in color mosaic image simultaneously. • Exploit unique properties of human GI tract color images.

L

HARDWARE REALIZATION

Figure 5: Average entropy (left) and standard deviation (right) of 120 endoscopic images versus wavelet packet decomposition level.

METHODOLOGY

Original image

Table 2: The performance of the image compressor for different parameters (120 endoscopic images taken from http://www.gastrolab.net/ ). Parameters H subband Compression Rate (%) PSNR (dB) Quantizer sub-sampling factor 1 55.02 ∞ 2 65.49 51.3719 4 73.57 46.6142 8 78.35 41.0004 2 63.22 46.2585 4 68.05 41.2555 8 71.01 36.7500 4 2 78.59 40.6736 4 4 81.31 39.4471 4 8 82.95 36.6333

Figure 10: Hardware architecture of the image compressor implementation.

Drawback of WCE: Battery dies before it covers the whole gastrointestinal tract.

Level 0

EXPERIMENTAL RESULTS

Figure 9: Block diagram of the proposed image decompressor.

[Turcza and Duplaga, 2013] Turcza, P. and Duplaga, M. (2013). Hardware-efficient low-power image processing system for wireless capsule endoscopy. IEEE Journal of Biomedical and Health Informatics, 17(6):1046–1056. [Xie et al., 2006] Xie, X., Li, G., Chen, X., Li, X., and Wang, Z. (2006). A low-power digital ic design inside the wireless endoscopic capsule. IEEE Journal of Solid-State Circuits, 41(11):2390–2400. [Zhang and Wu, 2004] Zhang, N. and Wu, X. (2004). Lossless compression of color mosaic images. In ICIP ’04, International Conference on Image Processing.