Generalized VQ method for combined compression and estimation

Ajit Rao, David Jonathan Miller, Kenneth Rose, Allen Gersho

    Research output: Contribution to journalConference articlepeer-review

    28 Scopus citations


    In vector quantization, one approximates an input random vector, Y, by choosing from a finite set of values known as the codebook. We consider a more general problem where one may not have direct access to Y but only to some statistically related random vector X. We observe X and would like to generate an approximation to Y from a codebook of candidate vectors. This operation, called generalized vector quantization (GVQ), is essentially that of quantized estimation. An important special case of GVQ is the problem of noisy source coding wherein a quantized approximation of a vector, Y, is obtained from observation of its noise-corrupted version, X. The optimal GVQ encoder has high complexity. We overcome the complexity barrier by optimizing a structurally-constrained encoder. This challenging optimization task is solved via a probabilistic approach, based on deterministic annealing (DA), which overcomes problems of shallow local minima that trap simpler descent methods. We demonstrate the successful application of our method to the coding of noisy sources.

    Original languageEnglish (US)
    Pages (from-to)2032-2035
    Number of pages4
    JournalICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
    StatePublished - Jan 1 1996
    EventProceedings of the 1996 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP. Part 1 (of 6) - Atlanta, GA, USA
    Duration: May 7 1996May 10 1996

    All Science Journal Classification (ASJC) codes

    • Software
    • Signal Processing
    • Electrical and Electronic Engineering


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