Image Coding By Adaptive Vector Quantization With Least Squares Approximation

H. Sun; H. Hsu

doi:10.1117/12.947686

12 October 1988 Image Coding By Adaptive Vector Quantization With Least Squares Approximation

H. Sun, H. Hsu

Proceedings Volume 0956, Piece Recognition and Image Processing; (1988) https://doi.org/10.1117/12.947686
Event: SPIE International Symposium on Optical Engineering and Industrial Sensing for Advance Manufacturing Technologies, 1988, Dearborn, MI, United States

Abstract

An image may have different local statistics, or different local properties of detailed content. According to the information theory, a fixed bit rate coding scheme will result in more distortion in higher-detail (or active) areas than in lower-detail (or non-active) areas. Consequently, an edge degradation occurs. In this paper, an adaptive vector quantization with least squares approximation method is proposed to address this problem. The scheme is based upon a variable block size. The basic idea is to partition the image blocks into two parts; high-detail and low-detail or active and non-active, according to the activity index measurement. The active blocks are further divided into smaller blocks. The values of pixels in the non-active block are approximated by a smaller number of samples with least spuares method. The two sets which are designed to have same dimensionality are used to form a vector set and vector quantized. Therefore, the non-active blocks can be coded with the same codebook as for the high-detailed subblocks, but less bits are needed. Experimental results show that the quality of reconstructed images is improved in this algorithm over non-adaptive VQ because the bits saved in two low-detailed areas are allocated to the high-detailed area where the edge could be found.

Citation Download Citation

H. Sun and H. Hsu "Image Coding By Adaptive Vector Quantization With Least Squares Approximation", Proc. SPIE 0956, Piece Recognition and Image Processing, (12 October 1988); https://doi.org/10.1117/12.947686

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