Trellis-Based Scalar-Vector Quantizer for Memoryless Sources

Abstract

This paper describes a structured vector quantization approach for stationary memoryless sources that combines the scalar-vector quantizer (SVQ) ideas (Laroia and Farvardin) with trellis coded quantization (Marcellin and Fischer). The resulting quantizer is called the trellis-based scalar-vector quantizer (TB-SVQ). The SVQ structure allows the TB-SVQ to realize a large boundary gain while the underlying trellis code enables it to achieve a significant portion of the total granular gain. For large block- lengths and powerful (possibly complex) trellis codes the TB-SVQ can, in principle, achieve the rate-distortion bound. As indicated by the results obtained here, even for reasonable block-lengths and relatively simple trellis codes, the TB-SVQ outperforms all other reasonable complexity fixed-rate quantizers.