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Proceedings Paper

Entropy-constrained mean-gain-shape vector quantization for image compression
Author(s): Michael L. Lightstone; Sanjit K. Mitra
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Paper Abstract

A method for optimal variable rate mean-gain-shape vector quantization (MGSVQ) is presented with application to image compression. Conditions are derived within an entropy- constrained product code framework that result in an optimal bit allocation between mean, gain, and shape vectors at all rates. An extension to MGSVQ called hierarchical mean-gain- shape vector quantization (HMGSVQ) is similarly introduced. By considering statistical dependence between adjacent means, this method is able to provide improvement in rate- distortion performance over traditional MGSVQ, especially at low bit rates. Simulation results are provided to demonstrate the rate-distortion performance of MGSVQ and HMGSVQ for image data.

Paper Details

Date Published: 16 September 1994
PDF: 12 pages
Proc. SPIE 2308, Visual Communications and Image Processing '94, (16 September 1994); doi: 10.1117/12.185981
Show Author Affiliations
Michael L. Lightstone, Univ. of California/Santa Barbara (United States)
Sanjit K. Mitra, Univ. of California/Santa Barbara (United States)


Published in SPIE Proceedings Vol. 2308:
Visual Communications and Image Processing '94
Aggelos K. Katsaggelos, Editor(s)

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