Entropy-constrained tree-structured vector quantizer design by the minimum cross entropy principle

Kenneth Rose, David Jonathan Miller, Allen Gersho

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations


We address the variable rate tree-structured vector quantizer design problem, wherein the rate is measured by the quantizer's entropy. For this problem, tree pruning via the Generalized Breiman-Friedman-Olshen-Stone algorithm obtains solutions which are optimal over the restricted solution space consisting of all pruned trees derivable from an initial tree. We develop a joint optimization method which is inspired by the deterministic annealing algorithm for data clustering, and which extends our previous work on tree-structured vector quantization. The method is based on the principle of minimum cross entropy, using informative priors to approximate the unstructured solution while imposing the structural constraint. As in the original deterministic annealing method, the number of distinct codevectors (and hence the tree) grows by a sequence of bifurcations in the process, which occur as solutions of a free energy minimization. Our method obtains performance gains over growing and pruning methods for variable rate quantization of Gauss-Markov and Gaussian mixture sources.

Original languageEnglish (US)
Title of host publicationProceedings of the Data Compression Conference
EditorsJames A. Storer, Martin Cohn
PublisherPubl by IEEE
Number of pages10
ISBN (Print)0818656379
StatePublished - Jan 1 1994
EventProceedings of the Data Compression Conference - Snowbird, UT, USA
Duration: Mar 29 1994Mar 31 1994


OtherProceedings of the Data Compression Conference
CitySnowbird, UT, USA

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications


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