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An Enhanced Vector Quantization Method for Image Compression with Modified Fuzzy Possibilistic C-Means using Repulsion
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 21 - Number 5 |
| Year of Publication: 2011 |
| Authors: S. Sathappan, Dr. S. Pannirselvam |
10.5120/2505-3387
|
S. Sathappan, Dr. S. Pannirselvam . An Enhanced Vector Quantization Method for Image Compression with Modified Fuzzy Possibilistic C-Means using Repulsion. International Journal of Computer Applications. 21, 5 ( May 2011), 27-34. DOI=10.5120/2505-3387
Abstract
Since the development of internet and multimedia, image compression is emerging in all the fields like pattern recognition, image processing, system modeling, data mining, etc. Compression techniques have become the most concentrated area in the fields of computer. Image compression is a technique of efficiently coding digital image to reduce the number of bits required in representing an image. Many image compression techniques presently exist for the compression of different types of images. In this paper, Vector Quantization based compression technique is established with Modified Fuzzy Possibilistic C-Means (MFPCM) with repulsion. Repulsion technique aims to reduce the intra-cluster distances and also increases the inter-cluster distances. The residual codebook is used in this proposed approach which eliminates the distortion in the reconstructed image and thus enhancing the image quality. Moreover, the proposed technique replaces LBG algorithm with the modified fuzzy possiblistic c-means algorithm in the codebook generation. Experimental results on standard image Lena show that the proposed scheme can give a reconstructed image with higher PSNR value than the existing image compression techniques.
References
- Yanfeng Zhang, Xiaofei Xu and Yunming Ye, “An Agglomerative Fuzzy K-means clustering method with automatic selection of cluster number,” 2nd International Conference on Advanced Computer Control (ICACC), Vol. 2, pp. 32-38, 2010.
- Xiao-Hong Wu and Jian-Jiang Zhou, "Possibilistic Fuzzy c-Means Clustering Model Using Kernel Methods," International Conference on Intelligent Agents, Web Technologies and Internet Commerce, Vol. 2, Publication Year: 2005 , Pp. 465 – 470.
- Zhang, Zhe Zhang, Junxi Xue and Huifeng, “Improved K-Means Clustering Algorithm,” Conference on Image and Signal Processing, Vol. 5, pp. 169-172, 2008.
- Yang Yan and Lihui Chen, "Hyperspherical possibilistic fuzzy c-means for high-dimensional data clustering", ICICS 2009. 7th International Conference on Information, Communications and Signal Processing, 2009, Publication Year: 2009 , Pp. 1-5
- Vuda. Sreenivasarao and Dr. S. Vidyavathi, "Comparative Analysis of Fuzzy C- Mean and Modified Fuzzy Possibilistic C -Mean Algorithms in Data Mining", IJCST Vol. 1, Issue 1, September 2010, Pp. 104-106
- R. M. Gray, “Vector quantization,” IEEE Acoustics, speech and Signal Processing Magazine, pp. 4-29, 1984.
- M. Goldberg, P. R. Boucher and S. Shlien, “Image Compression using adaptive vector quantization,” IEEE Transactions on Communication, Vol. 34, No. 2, pp. 180-187, 1986.
- Y. Linde, A. Buzo and R. M. Gray, “An algorithm for vector quantizer design,” IEEE Transactions on Communication, Vol. 28, No. 1, 1980, pp. 84 – 95.
- T.Kim, “Side match and overlap match vector quantizers for images,” IEEE Trans. Image. Process., vol.28 (1), pp.84-95, 1980.
- Z.M.Lu, J.S Pan and S.H Sun, “Image Coding Based on classified sidematch vector quantization,” IEICE Trans.Inf.&Sys., vol.E83-D(12), pp.2189-2192, Dec. 2000.
- Z.M.Lu, B.Yang and S.H Sun, “Image Compression Algorithms based on side-match vector quantizer with Gradient-Based classifiers,” IEICE Trans.Inf.&Sys., vol.E85-D(9), pp.1414- 1420, September. 2002.
- Chia-Hung Yeh, “Jigsaw-puzzle vector quantization for image compression” , Opt.Eng Vol.43, No.2, pp. 363-370, Feb-2004.
- C.H.Hsieh, and J.C Tsai, “Lossless compression of VQ index with search order Coding,” IEEE Trans. Image Processing, Vol.5, No. 11, pp. 1579- 1582, Nov. 1996.
- Chun-Yang Ho, Chaur-Heh Hsieh and Chung-Woei Chao, “Modified Search Order Coding for Vector Quantization Indexes,” Tamkang Journal of Science and Engineering, Vol.2, No.3, pp. 143- 148, 1999.
- K. Lung, “A cluster validity index for fuzzy clustering”, Pattern Recognition Letters 25(2005) 1275-1291.
- U.M. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy, “Advances in Knowledge Discovery and Data Mining”, AAAI Press and MIT Press, Menlo Park and Cambridge, MA, USA 1996.
- M.S. Yang, On a class of fuzzy classi4cation maximum likelihood procedures, Fuzzy Sets and Systems 57 (1993) 365–375.
- M.S. Yang, C.F. Su, On parameter estimation for normal mixtures based on fuzzy clustering algorithms, Fuzzy Sets and Systems 68 (1994) 13–28.
- W.L. Hung, M Yang, D. Chen,” Parameter selection for suppressed fuzzy c-means with an application to MRI segmentation”, Pattern Recognition Letters 2005.
- R.C. Gonzalez, R.E. Woods, Digital Image Processing, Pearson Education Pvt. Ltd., New Delhi, 2nd Edition.
- M. Antonini, M. Barlaud, P. Mathieu, I. Daubechies, Image Coding using Wavelet Transform, IEEE Transactions on Image Processing, Vol. 1, No. 2, pp. 205-220, April, 1992.
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