Trellis coded quantization of memoryless and Gauss-Markov sources

Abstract

Trellis-coded quantization (TCQ) is developed and applied to the encoding of memoryless and Gauss-Markov sources. The theoretical justification for the approach is alphabet-constrained rate distortion theory, which is a dual to the channel capacity argument that motivates trellis-coded modulation (TCM). The authors adopt the notions of signal set expansion, set partitioning, and branch labeling of TCM, but modify the techniques to account for the source distribution, to design TCQ coders of low complexity with excellent mean-squared-error (MSE) performance. For a memoryless uniform source, TCQ provides an MSE within 0.21 dB of the distortion-rate bound at all positive (integral) rates. The performance is superior to that promised by the coefficient of quantization for all of the best lattices known in dimensions 24 or less. For a memoryless Gaussian source, the TCQ performance at rates of 0.5, 1, and 2 b/sample is superior to all previous results the authors found in the literature. The encoding complexity of TCQ is very modest. TCQ is incorporated into a predictive coding structure for the encoding of Gauss-Markov sources. Simulation results for first-, second-, and third-order Gauss-Markov sources are presented. >