A general-tree-structured vector quantizer for image progressive coding

Recently, several tree-structured vector quantizers had been proposed. But almost all trees used are binary trees and hence the training samples contained in each node are forced to be divided into two clusters artificially. We present a general-tree-structured vector quantizer that is based on a genetic clustering algorithm. This genetic clustering algorithm can divide the training samples contained in each node into more natural clusters. A distortion threshold is used to guarantee the quality of coding. Also, the Huffman coding is used to achieve the optimal bit rate after the general-tree-structured coder was constructed. Progressive coding can be accomplished by given a series of distortion thresholds. An experiment result is given to illustrate the performance of this vector quantizer on image progressive coding. A comparison of the performance of this vector quantizer and the other two tree-structured vector quantizers is also given.