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Proceedings Paper

Image Coding By Adaptive Vector Quantization With Least Squares Approximation
Author(s): H. Sun; H. Hsu
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Paper 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.

Paper Details

Date Published: 12 October 1988
PDF: 7 pages
Proc. SPIE 0956, Piece Recognition and Image Processing, (12 October 1988); doi: 10.1117/12.947686
Show Author Affiliations
H. Sun, Fairleigh Dickinson University (United States)
H. Hsu, Fairleigh Dickinson University (United States)


Published in SPIE Proceedings Vol. 0956:
Piece Recognition and Image Processing
Wayne Wiitanen, Editor(s)

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