Delayed-Decision Binary Tree-Searched Vector Quantization For Image Compression

Abstract

A new tree-searched VQ scheme called delayed-decision binary tree-searched VQ is proposed in this paper. To alleviate the sub-optimal solution problem of binary tree-searched algorithms, it uses multipath search to find the best matching codevector in a binary-tree codebook. At each tree node, it examines the path error of the 2*M branches extended from M saved nodes, and Only the best M of these branches are saved for the next step. This procedure continues until the end of the tree is reached, and then the codevector of the best matched node among the final M saved nodes is used. In simulations, the delayed-decision algorithm is incorporated in a mean/residue binary tree-searched VQ. It is shown that, on the average, a 20% reduction of mean-square error is obtained when M=8. Therefore, the performance is much improved by better searching the same codebook at the expense of computational costs. Most of all, the image quality is improved without increasing the bit rate.