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Side-Match Vector Quantizers Using Neural Network Based Variance Predictor for Image Coding
Shuangteng Zhang
Pages - 119 - 129     |    Revised - 01-05-2011     |    Published - 31-05-2011
Volume - 5   Issue - 2    |    Publication Date - May / June 2011  Table of Contents
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KEYWORDS
Vector Quantization, Image Coding, Side Match, Neural Network
ABSTRACT
Side-match vector quantizer reduces bit-rates in image coding by using smaller-sized state codebooks generated from a master codebook through exploiting the correlations of neighboring vectors. This paper presents a new neural network based side-match vector quantization method for image coding. In this method, based on the variance of a vector which is predicted by a neural network, a subset of the codewords in the master codebook is selected for the side-matching to construct the state codebook for the encoding of the vector. This technique generates a lower encoding bit rate with a higher reconstructed image quality. Experimental results demonstrate that in terms of PSNR (Peak Signal-to-Noise Ratio) of the reconstructed images, the proposed method significantly outperforms the regular side-match vector quantizer, especially at lower coding bit-rates.
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Dr. Shuangteng Zhang
- United States of America
shuangteng.zhang@eku.edu