Adaptive initial blocks for improving gradient-match and side-match vector quantization of images

Abstract

The selection of initial blocks is one of the important issues in finite-state vector quantizers (FSVQs) for images. Conventional FSVQs use blocks located at fixed positions as initial blocks, which then are coded by the codewords in the super codebook. We propose an adaptive-initial-block (AIB) scheme to determine the initial blocks for gradient-match (GM) and side-match (SM) vector quantizers, which are two significant classes of FSVQs. According to the next-state functions used in GMVQs and SMVQs, the blocks with large boundary matching errors in original VQs are selected as the initial blocks. The error propagation effects can be reduced, and the coding performance is significantly improved. The simulation results show that the peak signal-tonoise ratios of coded images can be 2 dB higher than that of conventional GMVQs and SMVQs when the state codebook sizes are much smaller than that of the super codebook.