FUZZY VECTOR QUANTIZATION ALGORITHMS AND THEIR APPLICATION IN IMAGE COMPRESSION

被引:79
作者
KARAYIANNIS, NB
PAI, PI
机构
[1] Department of Electrical and Computer Engineering, University of Houston, Houston
关键词
D O I
10.1109/83.413164
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
This paper presents the development and evaluation of fuzzy vector quantization algorithms, These algorithms are designed to achieve the quality of vector quantizers provided by sophisticated but computationally demanding approaches, while capturing the advantages of the frequently used in practice k-means algorithm, such as speed, simplicity, and conceptual appeal, The uncertainty typically associated with clustering tasks is formulated in this approach by allowing the assignment of each training vector to multiple clusters in the early stages of the iterative codebook design process, A training vector assignment strategy is also proposed for the transition from the fuzzy mode, where each training 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 random initial codebook selection, The resulting algorithms are used in image compression based on vector quantization, This application provides the basis for evaluating the computational efficiency of the proposed algorithms and comparing the quality of the resulting codebook design with that provided by competing techniques.
引用
收藏
页码:1193 / 1201
页数:9
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