Fast vector quantizer using multiple sorted index tables

Abstract

Vector quantization (VQ) is a widely used data compression technique which exploits the correlation of the neighbouring samples. To overcome the high computational complexity problem, several fast vector quantization (FVQ) algorithms have been proposed with the quality of the reconstructed images preserved. The efficiency of an FVQ using triangular inequality elimination (TIE) greatly depends on the selection of an initial code vector in searching for the optimal code vector. We propose a TIE-based FVQ using multiple sorted index tables with respect to the anchor points. In the proposed FVQ, the search region does not depend on the initial code vector. Instead of reducing the search region in the conventional TIE-based methods, this approach is to incrementally extend the search region from the center position in the triangular inequalities. The search region is found in a presorted, augmented codebook. We found that the computational complexity of the proposed FVQ is lower than that of Li's (see LEEE Trans. CAS for Video Technology, vol.5, no.2, p.119-123, 1995) FVQ.