An efficient approach to lattice-based fixed-rate entropy-coded vector quantization

Abstract

In the absence of channel noise, variable-length quantizers perform better than fixed-rate Lloyd–Max quantizers for any source with a non-uniform density function. However, channel errors can lead to a loss of synchronization resulting in a propagation of error. To avoid having variable rate, one can use a vector quantizer selected as a sub-set of high probability points in the Cartesian product of a set of scalar quantizers and represent its elements with binary code-words of the same length (quantizer shaping). We choose these elements from a lattice, resulting in a higher quantization gain in comparison to simply using the Cartesian product of a set of scalar quantizers. We introduce a class of lattices which have a low encoding complexity, and at the same time result in a noticeable quantization gain. We combine the procedure of lattice encoding with that of quantizer shaping using hierarchical dynamic programming. In addition, by devising appropriate partitioning and merging rules, we obtain sub-optimum schemes of low complexity and small performance degradation. The proposed methods show a substantial improvement in performance and/or a reduction in the complexity with respect to the best known results. Copyright © 2003 AEI.