Vector Quantization Code Book Design Using Neural Networks
نویسنده
چکیده
The Kohonen Self-Organizing Feature Map algorithm is compared to the K-Means vector quantization algorithm. Computation and storage requirements are calculated for both algorithms. A new algorithm which takes advantage of the structured code book produced by Kohonen's algorithm is introduced. This algorithm ooers a signiicant computational savings over full-search vector quantiza-tion without imposing a storage cost penalty. The results of simulation studies are presented and the performance of the algorithms is compared .
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تاریخ انتشار 1990