EZW Coding System with Artificial Neural Networks

Authors: Saudagar Abdul Khader Jilani, Syed Abdul Sattar

Abstract:

Image compression plays a vital role in today-s communication. The limitation in allocated bandwidth leads to slower communication. To exchange the rate of transmission in the limited bandwidth the Image data must be compressed before transmission. Basically there are two types of compressions, 1) LOSSY compression and 2) LOSSLESS compression. Lossy compression though gives more compression compared to lossless compression; the accuracy in retrievation is less in case of lossy compression as compared to lossless compression. JPEG, JPEG2000 image compression system follows huffman coding for image compression. JPEG 2000 coding system use wavelet transform, which decompose the image into different levels, where the coefficient in each sub band are uncorrelated from coefficient of other sub bands. Embedded Zero tree wavelet (EZW) coding exploits the multi-resolution properties of the wavelet transform to give a computationally simple algorithm with better performance compared to existing wavelet transforms. For further improvement of compression applications other coding methods were recently been suggested. An ANN base approach is one such method. Artificial Neural Network has been applied to many problems in image processing and has demonstrated their superiority over classical methods when dealing with noisy or incomplete data for image compression applications. The performance analysis of different images is proposed with an analysis of EZW coding system with Error Backpropagation algorithm. The implementation and analysis shows approximately 30% more accuracy in retrieved image compare to the existing EZW coding system.

Keywords: Accuracy, Compression, EZW, JPEG2000, Performance.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1083935

References:

[1] J. Shapiro, "Embedded image coding using zerotrees of wavelet coefficients," IEEE Trans. Signal Processing, vol. 41, pp. 3445-3462, Dec 1993.
[2] S.Mallat and F.Falzon, "Analysis of low bit rate image transform coding," IEEE Trans. Signal Processing, vol. 46, pp. 1027-1042,Apr. 1998.
[3] Z. Xiong, K. Ramachandran, and M. Orchad, "Space-frequency quantization for wavelet image coding," IEEE Trans. Signal Processing, vol. 6, pp. 677 - 693, May. 1997.
[4] E. H. Adelson, E. Simoncelli, and R. Hingorani, "Orthogonal pyramid transforms for image coding," Proc. SPIE, vol.845, Cambridge, MA, Oct. 1987, pp.50-58.
[5] R. A. DeVore, B. Jawerth and B. J. Lucier, " Imagecompression through wavelet transform coding" IEEE Trans. Informat. Theory, vol 38, pp. 719 - 746, Mar. 1992.
[6] S. Mallat, "A theory for multiresolution signal decomposition: The wavelet representation," IEEE Trans. Pattern Anal. Mach. Intell., vol 37, pp. 2091 - 2110, Dec. 1990.
[7] G. K. Wallace, "The JPEG Still Picture Compression Standard," Commun. ACM, vol 34, pp. 30 - 44, Apr. 1991.
[8] Bryan E. Usevitch, " A tutorial on Modern Lossy Wavelet Image Compression: Foundations of JPEG 2000", IEEE signal processing magazine, 1053-5888, sep-2001.
[9] Athanassios. skodras, Charilaos Christopoulos and Touradj Ebrahimi, "The JPEG 2000 Still Image Compression Standard" IEEE signal processing magazine, 1053-5888, sep-2001.
[10] Colm Mulcahy "Image Compression using the Harr wavelet transform", spelman science and Math Journal.
[11] Tang Xianghong Liu Yang "An Image Compressing Algorithm Based on Classified Blocks with BP Neural Networks" International Conference on Computer Science and Software Engineering, Date: 12- 14 Dec. 2008 Volume: 4, On page(s): 819-822.
[12] Adnan Khashman, Kamil Dimililer " Image compression using neural networks and haar wavelet" WSEAS Transactions on Signal Processing Volume 4 , Issue 5 , May 2008, Pages 330-339.
[13] Andrea Basso, Murat Kunt "Autoassociative Neural Networks for Image Compression" European Transactions on Telecommunications Volume 3 Issue 6, Sep 2008, Pages 593 - 598.
[14] Rafid Ahmed Khalil, "Digital Image Compression Enhancement Using Bipolar Backpropagation Neural Networks" Al-Rafidain Engineering Vol.15 No.4, 2007.
[15] Khashman, A. Dimililer, K. "Neural Networks Arbitration for Optimum DCT Image Compression" EUROCON, The International Conference on "Computer as a Tool" Sep. 2007 On page(s): 151-156.
[16] Fard, M.M " A Co-evolutionary Competitive Multi-expert Approach to Image Compression with Neural Networks" Engineering of Intelligent Systems, 2006 IEEE International Conference On page(s): 1-5.
[17] Asraf, R. Akbar, M. Jafri, N. "Diagnostically Lossless Compression-2 of Medical Images" 1st Transdisciplinary Conference on Distributed Diagnosis and Home Healthcare, 2-4 April 2006 On page(s): 28-32.
[18] S. Anna Durai, and E. Anna Saro "Image Compression with Back- Propagation Neural Network using Cumulative Distribution Function" World Academy of Science, Engineering and Technology 2006.
[19] Karlik, Bekir "Medical Image Compression by using Vector Quantization Neural Network (VQNN) " Neural Network World January 1, 2006.
[20] S.B. Roy, K. Kayal, and J. Sil (India) "Edge Preserving Image Compression Technique using Adaptive Feed Forward Neural Network" Proceeding (462) European Internet and Multimedia Systems and Applications - 2005.
[21] Pedro Gutiérrez, Pascual Campoy "Image Compression by a Time Enhanced Neural Network" - 2005.
[22] Hong Wang Ling Lu Da-Shun Que Xun Luo "Image compression based on wavelet transform and vector quantization" International Conference on Machine Learning and Cybernetics, 2002, 4-5 Nov. 2002 Volume: 4, on page(s): 1778- 1780
[23] Christophe Amerijckx, Philippe Thissen "Image Compression by Self- Organized Kohonen Map " IEEE Trans. On Neural Networks, vol. 9, NO. 3, May 1998.