Fuzzy Clustering-Based Vector Quantization for Image Compression

The implementation of fuzzy clustering-based vector quantization (VQ) algorithms in image compression is related to three difficulties: (a) the dependence on initialization, (b) the reduction of the computational cost, and (c) the quality of the reconstructed image. In this paper, first we briefly review the existing fuzzy clustering techniques used in VQ. Second, we present a novel algorithm that utilizes two stages to deal with the aforementioned problems. In the first stage, we develop a specialized objective function that incorporates the c-means and the fuzzy c-means in a uniform fashion. This strategy provides a tradeoff between the speed and the efficiency of the algorithm. The joint effect is the creation of hybrid clusters that possess crisp and fuzzy areas. In the second stage, we use a utility measure to quantify the contributions of the resulting clusters. Clusters with small utilities are relocated (i.e., migrated) to fuzzy areas of large clusters so that they can increase their utility and obtain a better local minimum. The algorithm is implemented in gray-scale image compression, where its efficiency is tested and verified.

[1]  George E. Tsekouras,et al.  Fast fuzzy vector quantization , 2010, International Conference on Fuzzy Systems.

[2]  Robert M. Gray,et al.  An Algorithm for Vector Quantizer Design , 1980, IEEE Trans. Commun..

[3]  Guobin Shen,et al.  Adaptive vector quantization with codebook updating based on locality and history , 2003, IEEE Trans. Image Process..

[4]  Jeng-Shyang Pan,et al.  Hadamard transform based fast codeword search algorithm for high-dimensional VQ encoding , 2004, The 2004 IEEE Asia-Pacific Conference on Circuits and Systems, 2004. Proceedings..

[5]  Shen Furao,et al.  An adaptive incremental LBG for vector quantization , 2006, Neural Networks.

[6]  Damianos Gavalas,et al.  Improved batch fuzzy learning vector quantization for image compression , 2008, Inf. Sci..

[7]  Shen-En Qian.  Fast vector quantization algorithms based on nearest partition set search , 2006, IEEE Transactions on Image Processing.

[8]  Giuseppe Patanè,et al.  The enhanced LBG algorithm , 2001, Neural Networks.

[9]  Nicolaos B. Karayiannis,et al.  An axiomatic approach to soft learning vector quantization and clustering , 1999, IEEE Trans. Neural Networks.

[10]  N.B. Karayiannis,et al.  Fuzzy vector quantization algorithms and their application in image compression , 1995, IEEE Trans. Image Process..

[11]  Jung Kim,et al.  Image compression using transformed vector quantization , 2002, Image Vis. Comput..

[12]  Bernd Fritzke,et al.  The LBG-U Method for Vector Quantization – an Improvement over LBG Inspired from Neural Networks , 1997, Neural Processing Letters.

[13]  George E. Tsekouras,et al.  A fuzzy vector quantization approach to image compression , 2005, Appl. Math. Comput..

[14]  James C. Bezdek,et al.  Fuzzy Kohonen clustering networks , 1994, Pattern Recognit..

[15]  Yi-Ching Liaw,et al.  Fast-searching algorithm for vector quantization using projection and triangular inequality , 2004, IEEE Transactions on Image Processing.

[16]  Myung Jin Bae,et al.  An Improvement of Modified K-Means Algorithm for Vector Quantizer Design , 1997 .

[17]  Xiangwei Kong,et al.  Fuzzy clustering algorithms based on resolution and their application in image compression , 2002, Pattern Recognit..

[18]  James C. Bezdek,et al.  An integrated approach to fuzzy learning vector quantization and fuzzy c-means clustering , 1997, IEEE Trans. Fuzzy Syst..