VECTOR QUANTIZATION OF IMAGES USING A FUZZY CLUSTERING METHOD

A novel approach with an incremental fuzzy clustering algorithm for vector quantization of images is proposed. For compression, a given image is divided into blocks of training vectors, and fuzzy clusters are generated progressively from those vectors. The obtained fuzzy clusters become the code-book from which the entire image is transformed into a sequence of indices. For decompression, the indices are decoded and the image is reconstructed. The advantages of our approach are that clusters generated are compact and dense, the real distribution of training vectors can be captured, and training vectors can be represented by prototyping clusters more appropriately. Experimental results have shown that our method can achieve a higher compression ratio and produce a smaller error than other methods.

[1]  Jeng-Shyang Pan,et al.  An efficient encoding algorithm for vector quantization based on subvector technique , 2003, IEEE Trans. Image Process..

[2]  Shie-Jue Lee,et al.  A neuro-fuzzy system modeling with self-constructing rule generationand hybrid SVD-based learning , 2003, IEEE Trans. Fuzzy Syst..

[3]  Allen Gersho,et al.  Vector quantization and signal compression , 1991, The Kluwer international series in engineering and computer science.

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

[5]  G. Blelloch.  Introduction to Data Compression * , 2022 .

[6]  Chip-Hong Chang,et al.  Self-organizing topological tree for online vector quantization and data clustering , 2005, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[7]  Francesco Camastra,et al.  A novel kernel method for clustering , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[8]  Shie-Jue Lee,et al.  Knowledge acquisition from input-output data by fuzzy-neural systems , 1998, SMC'98 Conference Proceedings. 1998 IEEE International Conference on Systems, Man, and Cybernetics (Cat. No.98CH36218).

[9]  James C. Bezdek,et al.  Two soft relatives of learning vector quantization , 1995, Neural Networks.

[10]  G Patane,et al.  Fully automatic clustering system , 2002, IEEE Trans. Neural Networks.

[11]  Kannan Panchapakesan.  Image processing through vector quantization , 2000 .

[12]  Martin T. Hagan,et al.  Neural network design , 1995 .

[13]  Howard C. Card,et al.  Vector quantization of images using modified adaptive resonance algorithm for hierarchical clustering , 2001, IEEE Trans. Neural Networks.

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

[15]  Pei-Yin Chen.  An efficient prediction algorithm for image vector quantization , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[16]  Klaus Obermayer,et al.  Soft nearest prototype classification , 2003, IEEE Trans. Neural Networks.

[17]  Dong-Chul Park,et al.  Weighted centroid neural network for edge preserving image compression , 2001, IEEE Trans. Neural Networks.

[18]  Nicolaos B. Karayiannis,et al.  Soft learning vector quantization and clustering algorithms based on non-Euclidean norms: single-norm algorithms , 2005, IEEE Transactions on Neural Networks.

[19]  R. W. Harris,et al.  A comparison of several vector quantization codebook generation approaches , 1993, IEEE Trans. Image Process..