Combined Source-Channel Vector Quantization Using Deterministic Annealing

In this paper, we present a new approach to combined source-channel vector quantization. The method, derived within information theory and probability theory, utilizes deterministic annealing to avoid some local minima that trap conventional descent algorithms such as the Generalized Lloyd Algorithm. The resulting vector quantizers satisfy the necessary conditions for local optimality for the noisy channel case. We tested our method against several versions of the noisy channel Generalized Lloyd Algorithm on stationary, first order Gauss-Markov sources with a binary symmetric channel. Our method outperformed other methods under all test conditions, with the gains over noisy channel GLA growing with the codebook size. The quantizers designed using deterministic annealing are also shown to behave robustly under channel mismatch conditions. As a comparison with a separate source-channel system, over a large range of test channel conditions, our method outperformed a bandwidth-equivalent system incorporating a Hamming code. Also, for severe channel conditions, our method produces solutions with explicit error control coding.