• DocumentCode
    2336007
  • Title

    Bayes risk-weighted vector quantization

  • Author

    Gray, Robert M.

  • Author_Institution
    Dept. of Electr. Eng., Stanford Univ., CA, USA
  • fYear
    1994
  • fDate
    27-29 Oct 1994
  • Firstpage
    3
  • Abstract
    Lossy compression and classification algorithms both attempt to reduce a large collection of possible observations into a few representative categories so as to preserve essential information. A framework for combining classification and compression into one or two quantizers is described along with some examples and related to other quantizer-based classification schemes
  • Keywords
    Bayes methods; vector quantisation; Bayes risk-weighted vector quantization; classification algorithms; lossy compression algorithms; quantizer-based classification; Bit rate; Decoding; Distortion measurement; Entropy; Hardware; Image coding; Lagrangian functions; Nearest neighbor searches; Signal to noise ratio; Vector quantization;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Information Theory and Statistics, 1994. Proceedings., 1994 IEEE-IMS Workshop on
  • Conference_Location
    Alexandria, VA
  • Print_ISBN
    0-7803-2761-6
  • Type

    conf

  • DOI
    10.1109/WITS.1994.513847
  • Filename
    513847
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2336007