Entropy-constrained mean-gain-shape vector quantization for image compression
Michael L. Lightstone, Sanjit K. Mitra · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1994
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.