International Journal of Research in Signal Processing, Computing & Communication System Design

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A Still Image Compression Scheme with Joint Probability Based Scanning of a Bit Plane Using Golomb-Rice Code


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1 School of Computer Applications, Lovely Professional University, Phagwara, Punjab, India
     

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In this paper, a modified JPEG2000 still image compression system has been proposed. A three level decomposition of Daubechies 9/7 Discrete Wavelet Transformation has been first applied to the entire input image. Then, scalar quantization is used to decrease and round off the transformed coefficients. The quantized coefficients are then subjected to bit modeling in each bit plane. A joint probability statistical model based significance selection has been proposed to select the significant bit for entropy coding with two scan coding technique. In this proposed work, after selecting all the significant bits in a particular bit plane, a geometrically distributed set of context is modeled and subjected to encode with Golomb-Rice coding to give compressed data. The decompression is effected with a simple, respective inverse operation. The proposed system has been experimented with standard benchmark images and the standard performance measures, Compression Ratio and Peak-Signal to Noise Ratio are used to evaluate the result.

Keywords

Bit-Plane Modeling, Geometrically Distributed, Golomb-Rice Coding, JPEG2000, Peak-Signal to Noise Ratio (PSNR), Scan Coding.
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  • A. Kiely, “Selecting the golomb parameter in rice coding,” The Interplanetary Network Progress Report, vol. 42, no. 159, pp. 1-18, 2004.

  • A. Kiely, and M. Klimesh, “Generalized golomb codes and adaptive coding of wavelet-transformed image subbands,” The Interplanetary Network Progress Report, pp. 42-154, 2003.

  • A. Said, “Comparative analysis of arithmetic coding computational complexity,” IEEE Data Compression Conference, 23-25 March 2004.

  • C. Chrysafis, and A. Ortega, “Efficient context-based entropy coding for lossy wavelet image compression,” IEEE Data Compression Conference, pp. 241-250, 1997.

  • C.-H. Son, J.-W. Kim, S.-G. Song, and S.-M. Parklow, “Low complexity embedded compression algorithm for reduction of memory size and bandwidth requirements in the JPEG2000 encoder,” IEEE Transactions on Image Processing, vol. 56, pp. 65-73, 2011.

  • C.-C. Chang, and Y.-P. Lai, “An enhancement of JPEG still image compression with adaptive linear regression and golomb-rice coding,” Ninth International Conference on Hybrid Intelligent Systems, vol. 3, pp. 35-40, April 2009.

  • D. Taubman, “High performance scalable image compression with EBCOT,” IEEE Transaction on Image Processing, vol. 9, no. 7, pp. 1158-1170, July 2000.

  • D. S. Taubman, and M. Marcellin, “JPEG2000 - Image compression fundamentals, standards and practice,” The Springer International Series in Engineering and Computer Science, vol. 642, 2002.

  • H. ZainEldin, M. A. Elhosseini, and H. A. Ali, “Image compression algorithms in wireless multimedia sensor networks: A survey,” Ain Shams Engineering Journal, vol. 6, no. 2, pp. 481-490, June 2015.

  • H. S. Malvar, “Adaptive run-length/golomb-rice encoding of quantized generalized Gaussian sources with unknown statistics,” Data Compression Conference, vol. 6, pp. 23-32, 2000.

  • H.-S. Kim, J. Lee, H. Kim, S. Kang, and W. C. Park, “A lossless color image compression architecture using a parallel golomb-rice hardware CODEC,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 21, no. 11, pp. 1581-1587, November 2011.

  • J. Chen, “Context modeling based on context quantization with application in wavelet image coding,” IEEE Transaction on Image Processing, vol. 13, no. 1, pp. 26-32, January 2004.

  • J. Liu, and P. Moulin, “Analysis of interscale and intrascale dependencies between image wavelet coefficients,” International Conference on Image Processing, pp. 531-542, 2014.

  • J. M. Shapiro, “Embedded image coding using zerotrees of wavelet coefficients,” IEEE Transaction on Signal Processing, vol. 41, no. 12, pp. 3445-3462, December 1993.

  • J. S. Walker, and T. Q. Nguyen, “Wavelet-based image compression,” The Transform and Data Compression Handbook, CRC Press LLC, 2001.

  • M. Long, and H.-M. Tai, “Region of interest coding for image compression,” IEEE Transaction on Circuits and Systems, vol. 2, pp. 172-175, August 2002.

  • M. W. Marcellina, M. A. Lepleyb, A. Bilgina, T. J. Flohrc, T. T. Chinend, and J. H. Kasner, “An overview of quantization in JPEG 2000,” Signal Processing: Image Communication, vol. 17, pp. 73-84, 2002.

  • M. Yang, and N. Bourbakis, “An overview of lossless digital image compression techniques,” IEEE Transaction on Circuits and Systems, vol. 2, pp. 1099-1102, August 2002.

  • M. Rhu, and I.-C. Park, “Optimization of arithmetic coding for JPEG2000,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 20, no.3, pp. 446-451, March 2010.

  • P. G. Howard, and J. R. S. Vitter, “Practical implementations of arithmetic coding,” Image and Text Compression, vol. 176, pp. 85-112, 2005.

  • R. W. Buccigrossi, and E. P. Simoncelli, “Image compression via joint statistical characterization in the wavelet domain,” IEEE Transaction on Signal Processing, vol. 8, no. 12, pp. 1688-1701, December 1999.

  • R. Buckley, “JPEG 2000 - A Practical Digital Compression Standard,” Ph.D, DPC Technology Watch Series, Report 08-01, February 2008.

  • R. Zhang, R. Yu, Q. Sun, and L. W.-C. Wong, “A new bit-plane entropy coder for scalable image coding,” IEEE International Conference on Multimedia and Expo. (ICME 2005), pp. 237-240, 2005.

  • M. R. T. P. Seenu, and J. A. Linsely, “Analysis of lossless image compression using VLSI-oriented FELICS algorithm,” 2011 International Conference on Signal Processing, Communication, Computing and Networking Technologies (ICSCCN), pp. 623-628, July 2011.

  • T.-H. Tsai ,Y.-H. Lee, and Y.-Y. Lee, “Design and analysis of high-throughput lossless image compression engine using VLSI-oriented FELICS algorithm,” IEEE Transactions on Very Large Scale Integration (VLSI) Systems, vol. 18, no. 1, pp. 39-52, January 2010.

  • T. Nguyen, D. Marpe, H. Schwarz, and T. Wiegand, “Reduced complexity entropy coding of transform coefficient levels using truncated golomb-rice codes in video compression,” 18th IEEE International Conference on Image Processing, pp. 345-353, 2011.

  • M. J. Weinberger, G. Seroussi, and G. Sapiro, “LOCO-I: A low complexity, context-based, lossless image compression algorithm,” IEEE Data Compression Conference, pp. 140-149, 1996.

  • X. Delaunay, M. Chabert, G. Morin, and V. Charvillat, “Bit-plane analysis and contexts combining of JPEG2000 contexts for on-board satellite image compression,” IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 1057-1060, 2007.

  • X. Wu, “High-order context modeling and embedded conditional entropy coding of wavelet coefficients for image compression,” International Conference on Signals, Systems, and Computers, pp. 1378-1382, November 1997.

  • Y. Wiseman, “The still image lossy compression standard - JPEG and enhancement of JPEG compression for GPS images,” International Journal of Multimedia and Ubiquitous Engineering, vol. 10, no. 7, pp. 255-264, 2015.

  • Z. Xiong, K. Ramchandran, and M. T. Orchard, “Space-frequency quantization for wavelet image coding,” IEEE Transaction on Signal Processing, vol. 6, no. 5, pp. 677-693, May 1997.

  • L.-B. Zhang, X.-C. Yu, and S.-H. Wang, “New region of interest image coding based on multiple bitplanes up-down shift using improved SPECK algorithm,” International Conference on Innovative Computing, Information and Control, vol. 3, pp. 629-632, September 2006.

  • Z. Liu, and L. J. Karam, “Mutual information-based analysis of JPEG2000 contexts,” IEEE Transactions on Image Processing, vol. 14, no. 4, pp. 156-164, 2005.


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  • A Still Image Compression Scheme with Joint Probability Based Scanning of a Bit Plane Using Golomb-Rice Code

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Authors

Anu Priya
School of Computer Applications, Lovely Professional University, Phagwara, Punjab, India
Anupama
School of Computer Applications, Lovely Professional University, Phagwara, Punjab, India

Abstract


In this paper, a modified JPEG2000 still image compression system has been proposed. A three level decomposition of Daubechies 9/7 Discrete Wavelet Transformation has been first applied to the entire input image. Then, scalar quantization is used to decrease and round off the transformed coefficients. The quantized coefficients are then subjected to bit modeling in each bit plane. A joint probability statistical model based significance selection has been proposed to select the significant bit for entropy coding with two scan coding technique. In this proposed work, after selecting all the significant bits in a particular bit plane, a geometrically distributed set of context is modeled and subjected to encode with Golomb-Rice coding to give compressed data. The decompression is effected with a simple, respective inverse operation. The proposed system has been experimented with standard benchmark images and the standard performance measures, Compression Ratio and Peak-Signal to Noise Ratio are used to evaluate the result.

Keywords


Bit-Plane Modeling, Geometrically Distributed, Golomb-Rice Coding, JPEG2000, Peak-Signal to Noise Ratio (PSNR), Scan Coding.

References