Entropy-constrained tree-structured vector quantizer design

Current methods for the design of pruned or unbalanced tree-structured vector quantizers such as the Generalized Breiman-Friedman-Olshen-Stone (GBFOS) algorithm are effective, but suffer from several shortcomings. We identify and clarify issues of suboptimality including greedy growing, the suboptimal encoding rule, and the need for time sharing between quantizers to achieve arbitrary rates, We then present the leaf-optimal tree design (LOTD) method which, with a modest increase in design complexity, alters and reoptimizes tree structures obtained from conventional procedures. There are two main advantages over existing methods. First, the optimal entropy-constrained nearest-neighbor rule is used for encoding at the leaves; second, explicit quantizer solutions are obtained at all rates without recourse to time sharing. We show that performance improvement is theoretically guaranteed. Simulation results for image coding demonstrate that close to 1 dB reduction of distortion for a given rate can be achieved by this technique relative to the GBFOS method.