A method for rate-distortion optimal variable rate mean-gain-shape vector quantization (MGSVQ) is presented with application to image compression, Conditions are derived within an entropy-constrainedproductcode fram...
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A method for rate-distortion optimal variable rate mean-gain-shape vector quantization (MGSVQ) is presented with application to image compression, Conditions are derived within an entropy-constrainedproductcode 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.
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