Vector quantization provides better ratedistortion performance over scalar quantization even for a random vector with independent dimensions. However, the design and implementation complexity of vector quantizers is much higher than that of scalar quantizers. To reduce the complexity while achieving performance close to optimal vector quantization and better than scalar quantization, we propose...

An algorithm recently introduced by Breiman, Friedman, Olshen, and Stone in the context of classification and regression trees is reinterpreted and extended to cover a variety of applications in source coding and modeling in which trees are involved. These include variable-rate and minimum-entropy tree-structured vector quantization, minimum expected cost decision trees, variable-order Markov m...