Title :
Low Power and Low Complexity Compressor for Video Capsule Endoscopy
Author :
Khan, Tareq Hasan ; Wahid, Khan A.
Author_Institution :
Dept. of Electr. & Comput. Eng., Univ. of Saskatchewan, Saskatoon, SK, Canada
Abstract :
The main challenge in video capsule endoscopic system is to reduce the area and power consumption while maintaining acceptable video reconstruction. In this paper, a subsample-based data compressor for video endoscopy application is presented. The algorithm is developed around the special features of endoscopic images that consists of a differential pulse-coded modulation (DPCM) followed by Golomb-Rice coding. Based on the nature of endoscopic images, several subsampling schemes on the chrominance components are applied. This video compressor is designed in a way to work with any commercial low-power image sensors that outputs image pixels in a raster scan fashion, eliminating the need of memory buffer and temporary storage (as needed in transform coding schemes). An image corner clipping algorithm is also introduced. The reconstructed images have been verified by five medical doctors for acceptability. The proposed low-complexity design is implemented in a 0.18 μm CMOS technology and consumes 592 standard cells, 0.16 × 0.16 mm silicon area, and 42 μW of power. Compared to other algorithms targeted to video capsule endoscopy, the proposed raster-scan-based scheme performs strongly with a compression ratio of 80% and a very high reconstruction peak signal-to-noise ratio (over 48 dB).
Keywords :
CMOS image sensors; cellular arrays; compressors; endoscopes; image reconstruction; medical image processing; power consumption; pulse code modulation; 592 standard cells; CMOS technology; Golomb-Rice coding; chrominance components; commercial low-power image sensors; compression ratio; differential pulse-coded modulation; endoscopic imaging; image corner clipping algorithm; image pixels; image reconstruction; low complexity compressor; low power compressor; memory buffer; power consumption; raster-scan-based scheme; signal-noise ratio; silicon area; subsample-based data compressor; video capsule endoscopic system; video reconstruction; Image coding; Image color analysis; Image reconstruction; Medical diagnostic imaging; Pixel; Streaming media; DPCM; VLSI implementation; golomb coding; image compression; video capsule endoscopic application;
Journal_Title :
Circuits and Systems for Video Technology, IEEE Transactions on
DOI :
10.1109/TCSVT.2011.2163985