FUZZY VECTOR QUANTIZATION ALGORITHMS AND THEIR APPLICATION IN IMAGE COMPRESSION

This paper presents the development and evaluation of fuzzy vector qua ntization algorithms, These algorithms are designed to achieve the qua lity of vector quantizers provided by sophisticated but computationall y demanding approaches, while capturing the advantages of the frequent ly used in practice k-means algorithm, such as speed, simplicity, and conceptual appeal, The uncertainty typically associated with clusterin g tasks is formulated in this approach by allowing the assignment of e ach training vector to multiple clusters in the early stages of the it erative codebook design process, A training vector assignment strategy is also proposed for the transition from the fuzzy mode, where each t raining vector can be assigned to multiple clusters, to the crisp mode , where each training vector can be assigned to only one cluster, Such a strategy reduces the dependence of the resulting codebook on the ra ndom initial codebook selection, The resulting algorithms are used in image compression based on vector quantization, This application provi des the basis for evaluating the computational efficiency of the propo sed algorithms and comparing the quality of the resulting codebook des ign with that provided by competing techniques.