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Proceedings Paper

Fractal image compression based on visual perception
Author(s): Jer-Sen Chen
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Paper Abstract

Self-similarities, commonly explored in fractal image compression, are usually translated into matches between two pools, the range and the domain blocks, which are different partitions of the same image to be encoded. Simple transformations on the domain blocks are used in order to obtain a better match. A root-mean-square error measure between a range block and a transformed domain block is used in the encoding process to quantify the performance of the matching process and subsequently the quality of the encoded imaged. Alternative measures can be used in fractal image compression to account for human visual perception. Simple strategies, such as block intensity weighting and block texture weighting, reduce perceptual degradation with only very little added computational cost. Weighted error measures in frequency domain, though computationally much more expensive, can provide a more natural model of visual perception such as the direct account for the contrast sensitivity function. A multiscale approach to encode the image details at different resolution is proposed to not only speed up the matching process between the range and the domain blocks but also provide a mechanism for multiscale representation.

Paper Details

Date Published: 20 April 1995
PDF: 8 pages
Proc. SPIE 2411, Human Vision, Visual Processing, and Digital Display VI, (20 April 1995); doi: 10.1117/12.207530
Show Author Affiliations
Jer-Sen Chen, Wright State Univ. (United States)


Published in SPIE Proceedings Vol. 2411:
Human Vision, Visual Processing, and Digital Display VI
Bernice E. Rogowitz; Jan P. Allebach, Editor(s)

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