Low-power DCT-based compressor for wireless capsule endoscopy

Discrete Cosine Transform (DCT) is known as one of the most significant, fundamental, and prevailing transforms in data compression, watermarking, and medical digital image processing applications for secure storing, sending, and transmitting in cyberspace. This paper addresses a low power, area efficient, and low complexity structure of COordinate Rotation Digital Computer (CORDIC)-based DCT for applying to the Wireless Capsule Endoscopy (WCE) application. In this work, a novel structure is developed called low-power Lookahead (LPLA) CORDIC to overcome the problems of power supply restriction and small dimensions while maintaining the image quality in the WCE. The key idea in the proposed structure is that the successive computations of CORDIC algorithm are performed by utilizing the pipeline property of Lookahead (LA) CORDIC. This leads to employ a single common hardware for the two CORDIC outputs. Thus, the number of the adder and shifter blocks required for CORDIC computation decreases in the proposed structure. Moreover, we utilize pipelining, clock gating and data gating techniques which result in further reduction of power consumption. The simulation results by TSMC 0.13 m technology at 1.2v power supply demonstrate that the proposed LPLA CORDIC-based DCT structure averagely consumes 64% of the power, 43% of the area, and 8% of the power-delay-product (PDP) of the most efficient structures reported in the literature. Furthermore, by applying the proposed method to JPEG compression standard, it is shown that the reconstructed images of the proposed structure have favorable quality and compression ratio, in addition to achieving lower power consumption and area overhead. According to the aforementioned features, the proposed LPLA CORDIC-based DCT structure is suitable for utilizing in the compressor part of wireless endoscopy capsule and other low-power and high-quality systems which are battery-based. Display Omitted In this paper DCT-Based Compressor using LPLA CORDIC in WCE application is proposed.The main idea is based on employing a single common hardware for two CORDIC outputs.Clock gating and pipeline techniques is used for further reduction in power and glitch.Number of adder and shifter blocks are reduced compared with other known structures.Simulation results show 64%, 43% and 8% reduction in Power, area and PDP respectively.

[1]  Dong Sam Ha,et al.  On the low-power design of DCT and IDCT for low bit-rate video codecs , 2001, Proceedings 14th Annual IEEE International ASIC/SOC Conference (IEEE Cat. No.01TH8558).

[2]  Liyi Xiao,et al.  CORDIC Based Fast Radix-2 DCT Algorithm , 2013, IEEE Signal Processing Letters.

[3]  G.S. Moschytz,et al.  Practical fast 1-D DCT algorithms with 11 multiplications , 1989, International Conference on Acoustics, Speech, and Signal Processing,.

[4]  Jack E. Volder.  The CORDIC Trigonometric Computing Technique , 1959, IRE Trans. Electron. Comput..

[5]  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).

[6]  Shanq-Jang Ruan,et al.  A computationally efficient high-quality cordic based DCT , 2006, 2006 14th European Signal Processing Conference.

[7]  P. Turcza,et al.  Low-Power Image Compression for Wireless Capsule Endoscopy , 2007, 2007 IEEE International Workshop on Imaging Systems and Techniques.

[8]  Yen-wei Chen,et al.  3D DWT-DCT based multiple watermarks for medical volume data robust to geometrical attacks , 2011, 2011 International Conference on Electronics, Communications and Control (ICECC).

[9]  Jean-Marie Moureaux,et al.  Design and performance analysis of a zonal DCT-based image encoder for Wireless Camera Sensor Networks , 2012, Microelectron. J..

[10]  Abdellatif Mtibaa,et al.  Crypto-compression of medical image based on DCT and chaotic system , 2014, 2014 Global Summit on Computer & Information Technology (GSCIT).

[11]  V. Konda,et al.  Wireless Capsule Endoscopy , 2020, Geriatric Gastroenterology.

[12]  Liyi Xiao,et al.  CORDIC based fast algorithm for power-of-two point DCT and its efficient VLSI implementation , 2014, Microelectron. J..

[13]  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.

[14]  Zhongde Wang.  Fast algorithms for the discrete W transform and for the discrete Fourier transform , 1984 .

[15]  Jean-Marie Moureaux,et al.  Fast zonal DCT-based image compression for Wireless Camera Sensor Networks , 2010, 2010 2nd International Conference on Image Processing Theory, Tools and Applications.

[16]  M. G. Martini,et al.  Subjective and objective quality assessment in wireless teleultrasonography imaging , 2008, 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[17]  Min-Woo Lee,et al.  Reconfigurable CORDIC-Based Low-Power DCT Architecture Based on Data Priority , 2014, IEEE Transactions on Very Large Scale Integration (VLSI) Systems.

[18]  C. Chantrapornchai,et al.  On the comparison of digital image steganography algorithm based on DCT and wavelet , 2013, 2013 International Computer Science and Engineering Conference (ICSEC).

[19]  Ahmed Khoumsi,et al.  Energy-aware JPEG for Visual Sensor networks , 2008 .

[20]  P. Swain.  The future of wireless capsule endoscopy. , 2008, World journal of gastroenterology.

[21]  Amit Mehto,et al.  Adaptive Lossless Medical Image Watermarking Algorithm Based on DCT & DWT , 2016 .

[22]  Ahmed Khoumsi,et al.  Energy-efficient transmission scheme of JPEG images over Visual Sensor Networks , 2008, 2008 33rd IEEE Conference on Local Computer Networks (LCN).

[23]  Max Q.-H. Meng,et al.  A novel wireless capsule endoscope with JPEG compression engine , 2010, 2010 IEEE International Conference on Automation and Logistics.

[24]  Shanq-Jang Ruan,et al.  Low-power and high-quality Cordic-based Loeffler DCT for signal processing , 2007, IET Circuits Devices Syst..

[25]  Jinsang Kim,et al.  Low-power multiplierless DCT architecture using image correlation , 2004, IEEE Trans. Consumer Electron..

[26]  Pamela C. Cosman,et al.  Evaluating quality of compressed medical images: SNR, subjective rating, and diagnostic accuracy , 1994, Proc. IEEE.

[27]  M. Elhaji,et al.  Low complexity and efficient architecture of 1D-DCT based Cordic-Loeffler for wireless endoscopy capsule , 2015, 2015 IEEE 12th International Multi-Conference on Systems, Signals & Devices (SSD15).

[28]  Roman Maslennikov,et al.  Triangular systolic array with reduced latency for QR-decomposition of complex matrices , 2006, 2006 IEEE International Symposium on Circuits and Systems.

[29]  R. Shamir,et al.  Wireless capsule endoscopy of the small intestine in children. , 2015, Journal of pediatric gastroenterology and nutrition.

[31]  P. Swain,et al.  Wireless capsule endoscopy. , 2002, The Israel Medical Association journal : IMAJ.

[32]  A. Ajala Funmilola,et al.  Comparative analysis between discrete cosine transform and wavelet transform techniques for medical image compression , 2015, International Conference on Computer Vision and Image Analysis Applications.

[33]  Zhou Wang,et al.  Multiscale structural similarity for image quality assessment , 2003, The Thrity-Seventh Asilomar Conference on Signals, Systems & Computers, 2003.

[34]  Surapan Airphaiboon,et al.  Medical image encryption based on DCT-DWT domain combining 2D-DataMatrix Barcode , 2015, 2015 8th Biomedical Engineering International Conference (BMEiCON).

[35]  Kevin Curran,et al.  Digital image steganography: Survey and analysis of current methods , 2010, Signal Process..

[36]  Jongsun Park,et al.  Sign-select lookahead CORDIC based high-speed QR decomposition architecture for MIMO receiver applications , 2011 .

[37]  Lan-Rong Dung,et al.  An ultra-low-power image compressor for capsule endoscope , 2006, Biomedical engineering online.

[38]  G. Pradeepkumar,et al.  Effective watermarking algorithm to protect Electronic Patient Record using image transform , 2013, 2013 International Conference on Information Communication and Embedded Systems (ICICES).

[39]  P Swain,et al.  Wireless endoscopy. , 2000, Gastrointestinal endoscopy.

[40]  S.A. White,et al.  Applications of distributed arithmetic to digital signal processing: a tutorial review , 1989, IEEE ASSP Magazine.

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

[42]  Earl E. Swartzlander,et al.  Merged CORDIC algorithm , 1995, Proceedings of ISCAS'95 - International Symposium on Circuits and Systems.

[43]  Amit Kumar Singh,et al.  Secure Hybrid Robust Watermarking Technique for Medical Images , 2015 .

[44]  P. Kaviya,et al.  Performance Analysis of 32 Bit Array Multiplier with a Carry Look-Ahead Adder and with a Carry Save Adder , 2015 .

[45]  Wen-Hsiung Chen,et al.  A Fast Computational Algorithm for the Discrete Cosine Transform , 1977, IEEE Trans. Commun..

[46]  Gregory K. Wallace,et al.  The JPEG still picture compression standard , 1992 .

[47]  R. Siezen,et al.  others , 1999, Microbial Biotechnology.

[48]  Yen-Yu Chen.  Medical image compression using DCT-based subband decomposition and modified SPIHT data organization , 2007, Int. J. Medical Informatics.

[49]  Hai Huang,et al.  A novel CORDIC based unified architecture for DCT and IDCT , 2012, 2012 International Conference on Optoelectronics and Microelectronics.

[50]  B. Lee.  A new algorithm to compute the discrete cosine Transform , 1984 .

[51]  Tetsuya Nakamura,et al.  Capsule endoscopy: past, present, and future , 2008, Journal of Gastroenterology.

[52]  M. Mohanapriya,et al.  Area, Delay And Power Comparison Of Adder Topologies , 2012, VLSIC 2012.

[54]  Ahmed Khoumsi,et al.  Modeling and Adapting JPEG to the Energy Requirements of VSN , 2008, 2008 Proceedings of 17th International Conference on Computer Communications and Networks.