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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
Vector Quantization, Image Coding, Side Match, Neural Network
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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A. Gersho, R. M. Gray, Vector Quantization and Signal, Compression, Kluwer Academic Publishers, 1992.
C. C. Chang, F. C. Shiue, T. S. Chen, “Pattern-based side match vector quantization for image compression”, Imaging Science Journal, vol. 48, no. 2, pp. 63-76, 2000.
H. T. Chang and C. J. Kuo, “Iteration-free fractal image coding based on efficient domain pool design”, IEEE Tran. Image Processing, vol. 9, pp.329-339, 2000.
H. T. Chang, “Gradient match and side match fractal vector quantizers for images”, IEEE Trans. Image Process., vol. 11, no. 1, pp. 1-9, 2002.
H. T. Chang, “Gradient match vector quantizers for images”, Opt. Eng., vol. 39, no. 8, pp.2046-2057, 2000.
H. Wei, P. Tsai and J. Wang, “Three-sided side match vector quantization”, IEEE Trans. Circuits and Systems for Video Technology, vol. 10, no. 1, pp. 51–58, 2000.
K. Sayood, Introduction to data compression, Morgan Kaufmann Publishers, San Francisco, CA 1996.
M. H. Hassoun, Fundamentals of Artificial Neural Network, MIT Press, Cambridge, MA, 1995.
N. M. Nasrabadi, R. A. King, “Image coding using vector quantization: a review”, IEEE Tran. Communications, vol. 36, no. 8, pp. 957-971, 1988.
R. F. Chang and W. -T. Chen, “Image coding using variable-rate side-match finite-state vector quantization”, IEEE Tran. Image Processing, vol. 2, no. 1, pp. 104-108, 1993.
R. M. Gray, “Vector quantization”, IEEE ASSP Magazine 1, pp. 4-29, 1984.
S. B. Yang and L. Y. Tseng, “Smooth side-match classified vector quantizer with variable block size”, IEEE Tran. Image Processing, vol. 10, no. 5, pp. 677-685, 2001.
S. B. Yang, “New variable-rate finite state vector quantizer for image coding”, Opt. Eng., vol. 44, no. 6, 067001, 2005.
S. B. Yang, “Smooth side-match weighted vector quantiser with variable block size for image coding”, IEE Proc. Vis. Image Signal Processing, vol. 152, no. 6, pp. 763-770, 2005.
T. Kim, “Side match and overlap match vector quantizers for images”, IEEE Trans. Image Process., vol. 1, no. 2, pp. 170 -185, 1992.
Y. Linde, A. Buzo and R. M. Gray, “An algorithm for vector quantization design”, IEEE Trans. Communications, vol. 28, pp. 84-95, 1980.
Z. M. LU, B. Yang, S. H. SUN, “Image compression algorithms based on side-match vector quantizer with gradient-based classifiers”, IEICE TRAN. Information and Systems, vol. E85- D, no.9, pp.1409-1415, 2002.
Dr. Shuangteng Zhang
- United States of America