Entropy-constrained mean-gain-shape vector quantization for image compression

Michael L. Lightstone; Sanjit K. Mitra

doi:10.1117/12.185981

16 September 1994 Entropy-constrained mean-gain-shape vector quantization for image compression

Michael L. Lightstone, Sanjit K. Mitra

Proceedings Volume 2308, Visual Communications and Image Processing '94; (1994) https://doi.org/10.1117/12.185981
Event: Visual Communications and Image Processing '94, 1994, Chicago, IL, United States

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.

Citation Download Citation

Michael L. Lightstone and Sanjit K. Mitra "Entropy-constrained mean-gain-shape vector quantization for image compression", Proc. SPIE 2308, Visual Communications and Image Processing '94, (16 September 1994); https://doi.org/10.1117/12.185981

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