EZW Coding System with Artificial Neural Networks

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.

Authors:

• Saudagar Abdul Khader Jilani
• Syed Abdul Sattar

Keywords:

• Accuracy
• Compression
• EZW
• JPEG2000
• Performance.

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.