• DocumentCode
    1007766
  • Title

    A structured fixed-rate vector quantizer derived from a variable-length scalar quantizer. II. Vector sources

  • Author

    Loroia, Rajiv ; Farvardin, Nariman

  • Author_Institution
    AT&T Bell Lab., Murray Hill, NJ, USA
  • Volume
    39
  • Issue
    3
  • fYear
    1993
  • fDate
    5/1/1993 12:00:00 AM
  • Firstpage
    868
  • Lastpage
    876
  • Abstract
    For Pt.I see ibid., vol.39, no.3, p.851-67 (1993). The fixed-rate scalar-vector quantizer (SVQ) for quantizing stationary memoryless sources is extended to a specific type of vector source in which each component is a stationary memoryless scalar subsource independent of the other components. Algorithms for the design and implementation of the original SVQ are modified to apply to this case. The resulting SVQ, referred to as the extended SVQ (ESVQ), is then used to quantize stationary sources with memory (with known autocorrelation function). Numerical results are presented for the quantization of first-order Gauss-Markov sources using this scheme. It is shown that the ESVQ-based scheme performs very close to entropy-coded transform quantization while maintaining a fixed-rate output and outperforms the fixed-rate scheme that uses scalar Lloyd-Max quantization of the transform coefficients. It is also shown that this scheme performs better than implementable vector quantizers, especially at high rates
  • Keywords
    coding errors; vector quantisation; extended SVQ; first-order Gauss-Markov sources; scalar-vector quantizer; stationary memoryless scalar subsource; structured fixed-rate vector quantizer; variable-length scalar quantizer; vector source; Algorithm design and analysis; Autocorrelation; Bridges; Decorrelation; Gaussian processes; Helium; Karhunen-Loeve transforms; Polynomials; Quantization; Vectors;
  • fLanguage
    English
  • Journal_Title
    Information Theory, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    0018-9448
  • Type

    jour

  • DOI
    10.1109/18.256494
  • Filename
    256494