The Application of Fuzzy Hopfield Neural Network to Design Better Codebook for Image Vector Quantization

摘要

In this paper, the application of an unsupervised parallel approach called the Fuzzy Hopfield Neural Network (FHNN) for vector qunatization in image compression is pro- posed. The main purpose is to embed fuzzy reasoning strategy into neural networks so that on-line learning and parallel im- plementation for codebook design are feasible. The object is to cast a clustering problem as a minimization process where the criterion for the optimum vector qunatization is chosen as the minimization of the average distortion between training vectors.