Storage- and entropy-constrained classified vector quantization

Abstract

This paper presents a novel variable-rate classified vector quantizer (CVQ) design algorithm for image coding. The design algorithm, termed storage- and entropy-constrained classified vector quantization (SECCVQ) algorithm, is able to control the rate and the storage size of the CVQ. The algorithm allocates the rate and storage size available to each class of the VQ optimally so that the average distortion of the SECCVQ is minimized. Classification of image blocks is based on the edge orientation of each block in the wavelet domain. To reduce the arithmetic complexity of the CVQ, we employ a novel partial distance codeword search algorithm in the wavelet domain. Simulation results show that the SECCVQ enjoys a low average distortion, a low encoding complexity, a high visual perception quality, and is well-suited for very low bit rate image coding.