General-tree-structured vector quantizer for image progressive coding using the smooth side-match method

Several tree-structured vector quantizers have been proposed previously. However, owing to the fact that all trees used are fixed M-ary tree-structured, the training samples contained in each node must be artificially divided into a fixed number of clusters. This paper presents a general-tree-structured vector quantizer (GTSVQ) based on a genetic clustering algorithm that can divide the training samples contained in each node into more natural clusters. Also, the Huffman tree decoder is used to achieve the optimal bit rate after the construction of the general-tree-structured encoder. Progressive coding can be accomplished by giving a series of distortion or rate thresholds. Moreover, a smooth side-match method is presented to enhance the performance of coding quality according to the smoothness of the gray levels between neighboring blocks. The combination of the Huffman tree decoder and the smooth side-match method is proposed herein. Furthermore, the Lena image can be coded by GTSVQ with 0.198 bpp and 34.3 dB in peak signal-to-noise ratio.