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Proceedings Paper

Low-complexity image coding technique using visual patterns
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Paper Abstract

A visual pattern-based image compression technique is presented, in which 4 X 4 image blocks are classified in perceptually significant `shade' and `edge' classes. The proposed technique attempts to make use of neighboring blocks to encode a shade or an edge block by exploiting the Human Visual System characteristics. To reduce correlation present in the shade regions of an image, the mean intensity of a shade block is predicted from the neighboring shade blocks, and the error mean is computed. The error mean of a block is then encoded by choosing an appropriate quantizer based on its predicted mean. The quantizer has been designed after a careful study of the distribution of the error mean of shade blocks in test images, based on Weber's law, to maximize the compression ratio without introducing any visible error. Higher dimension shade blocks (8 X 8 and 16 X 16) are also formed, by merging adjacent shade blocks which further reduces the inter-block correlation. An edge block is assumed to contain two uniform intensity regions (low and high intensity) separated by a transition region. Hence, an edge block can be encoded by coding its edge pattern, low or high intensity and gradient. In order to reduce the inter-block correlation, the edge pattern and mean intensity (low or high) are predicted. The mean intensity of error is encoded by using an appropriate quantizer. Therefore, this technique achieves higher compression ratios, as compared to other visual pattern- based techniques, at very low computational complexity.

Paper Details

Date Published: 1 October 1998
PDF: 11 pages
Proc. SPIE 3460, Applications of Digital Image Processing XXI, (1 October 1998); doi: 10.1117/12.323200
Show Author Affiliations
Sunil Kumar, Univ. of Southern California (United States)
C.-C. Jay Kuo, Univ. of Southern California (United States)

Published in SPIE Proceedings Vol. 3460:
Applications of Digital Image Processing XXI
Andrew G. Tescher, Editor(s)

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