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This paper proposes an optimal maximum a posteriori probability decoder for variable-length encoded sources over binary symmetric channels that uses a novel state-space to deal with the problem of variable-length source codes in the decoder. This sequential, finite-delay, joint source-channel decoder delivers substantial improvements over the conventional decoder and also over a system that uses a standard forward error correcting code operating at the same over all bit rates. This decoder is also robust to inaccuracies in the estimation of channel statistics.

A central issue in the use of vector quantization (VQ) for speech or image compression is the specification of the codebook. The traditional method for VQ codebook design is to use the Generalized Lloyd Algorithm (GLA), an iterative optimization procedure where an initial codebook is continually refined so that each iteration reduces the distortion involved in coding a given training set. However, this algorithm easily gets trapped in local minima of the distortion, resulting in a suboptimal codebook. Simulated Annealing (SA) is a procedure that employs randomness in a search algorithm and tends to skirt relatively poor local minima in favor of better ones. By altering the GLA with techniques motivated by SA we are able to improve the quality of the codebwks by reducing the distortion involved in coding the training set. Results are presented in the context of image coding using VQ.