High compression efficiency image compression algorithm based on subsampling for capsule endoscopy

In this paper, a simple image compression algorithm is proposed for wireless capsule endoscopy. The proposed algorithm consists of new simplified YUV colour space, corner clipping, uniform quantization, subsampling, differential pulse code modulation and Golomb Rice code. Simplified YUV colour space is proposed based on special nature of endoscopic images and provide good results. The quantization and subsampling are used as lossy compression techniques and fixed Golomb-Rice code is used to encode residual value obtained after differential pulse code modulation operation. Here performance of different combination of quantization and subsampling techniques are analyzed based combination along with the proposed compression algorithm provides compression ratio of 89.3% and peak signal noise ratio of 45.1. the proposed algorithm provided better results as compared to various reported algorithms in literature in term of CR and PSNR.

[1]  Xie Xiang,et al.  Low-complexity and high-efficiency image compression algorithm for wireless endoscopy system , 2006, J. Electronic Imaging.

[2]  Lan-Rong Dung,et al.  A modified H.264 intra-frame video encoder for capsule endoscope , 2008, 2008 IEEE Biomedical Circuits and Systems Conference.

[3]  Ye Li,et al.  Low-complexity video compression for capsule endoscope based on compressed sensing theory , 2009, 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[4]  Mariusz Duplaga,et al.  Hardware-Efficient Low-Power Image Processing System for Wireless Capsule Endoscopy , 2013, IEEE Journal of Biomedical and Health Informatics.

[5]  Mariusz Duplaga,et al.  Near-lossless energy-efficient image compression algorithm for wireless capsule endoscopy , 2017, Biomed. Signal Process. Control..

[6]  Khan A. Wahid,et al.  Low Power and Low Complexity Compressor for Video Capsule Endoscopy , 2011, IEEE Transactions on Circuits and Systems for Video Technology.

[7]  Zhihua Wang,et al.  A Wireless Capsule Endoscope System With Low-Power Controlling and Processing ASIC. , 2009, IEEE transactions on biomedical circuits and systems.

[8]  Zhihua Wang,et al.  A Wireless Capsule Endoscope System With Low-Power Controlling and Processing ASIC , 2009, IEEE Transactions on Biomedical Circuits and Systems.

[9]  Khan A. Wahid,et al.  Lossless Compression in Bayer Color Filter Array for Capsule Endoscopy , 2017, IEEE Access.

[10]  Kinde A. Fante,et al.  Design and Implementation of Computationally Efficient Image Compressor for Wireless Capsule Endoscopy , 2015, Circuits Syst. Signal Process..

[11]  Guolin Li,et al.  A Low-Power Digital IC Design Inside the Wireless Endoscopic Capsule , 2006, IEEE Journal of Solid-State Circuits.

[12]  Sungho Kim,et al.  Robust Object Categorization and Segmentation Motivated by Visual Contexts in the Human Visual System , 2011, EURASIP J. Adv. Signal Process..

[13]  Khan A. Wahid,et al.  Are Current Advances of Compression Algorithms for Capsule Endoscopy Enough? A Technical Review , 2017, IEEE Reviews in Biomedical Engineering.

[14]  Khan A. Wahid,et al.  Implantable narrow band image compressor for capsule endoscopy , 2012, 2012 IEEE International Symposium on Circuits and Systems.

[15]  Long Chen,et al.  A New Hybrid Fault Diagnostic Method for Combining Dependency Matrix Diagnosis and Fuzzy Diagnosis Based on an Enhanced Inference Operator , 2016, Circuits Syst. Signal Process..

[16]  Khan A. Wahid,et al.  Design of a Lossless Image Compression System for Video Capsule Endoscopy and Its Performance in In-Vivo Trials , 2014, Sensors.

[17]  Zhihua Wang,et al.  An image compression algorithm for wireless endoscopy and its ASIC implementation , 2016, 2016 IEEE Biomedical Circuits and Systems Conference (BioCAS).

[18]  Khan A. Wahid,et al.  Subsample-based image compression for capsule endoscopy , 2011, Journal of Real-Time Image Processing.

[19]  Xin Liao,et al.  Robust Detection of Image Operator Chain With Two-Stream Convolutional Neural Network , 2020, IEEE Journal of Selected Topics in Signal Processing.

[20]  Lan-Rong Dung,et al.  A Subsample-Based Low-Power Image Compressor for Capsule Gastrointestinal Endoscopy , 2011, EURASIP J. Adv. Signal Process..

[21]  Khan A. Wahid,et al.  White and narrow band image compressor based on a new color space for capsule endoscopy , 2014, Signal Process. Image Commun..

[22]  Mariusz Duplaga,et al.  Energy-efficient image compression algorithm for high-frame rate multi-view wireless capsule endoscopy , 2016, Journal of Real-Time Image Processing.

[23]  Rajiv V. Joshi,et al.  The Impact of Statistical Leakage Models on Design Yield Estimation , 2011, VLSI Design.

[24]  Hua Liu,et al.  Development of a wireless capsule endoscope system based on field programmable gate array , 2017 .

[25]  Khan A. Wahid,et al.  Lossless and Low-Power Image Compressor for Wireless Capsule Endoscopy , 2011, VLSI Design.

[26]  João Manuel R. S. Tavares,et al.  Efficient parallelization on GPU of an image smoothing method based on a variational model , 2019, Journal of Real-Time Image Processing.

[27]  Zhihua Wang,et al.  A Near-Lossless Image Compression Algorithm Suitable for Hardware Design in Wireless Endoscopy System , 2007, EURASIP J. Adv. Signal Process..

[28]  Mariusz Duplaga,et al.  Low power FPGA-based image processing core for wireless capsule endoscopy , 2011 .

[29]  Daniel Teng,et al.  Efficient hardware implementation of an image compressor for wireless capsule endoscopy applications , 2008, 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence).