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Proceedings Paper

Scalable object-based compression algorithm for segmented space-telescope images
Author(s): Helen Boussalis; Charles Liu; Khosrow Rad; Jianyu Dong
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Paper Abstract

The noise-alike nature of astronomical images imposes a great challenge on compression. Due to the lack of correlation among adjacent pixels, it is very difficult to achieve good compression result using standard algorithms. To address the above challenge, a novel object-based compression method is proposed in this paper. Based on object analysis, the astronomical entities presented in the image are classified into two categories: clear and faint objects. For the former, a zerotree based wavelet compression algorithm is employed to achieve scalable coding; for the latter, a predictive coding method is used to preserve their location and intensity. The objective is to enhance the detection of faint object in astronomical images while providing a good overall visual effect. Experiment results demonstrate the superior performance of our proposed algorithm.

Paper Details

Date Published: 25 October 2004
PDF: 7 pages
Proc. SPIE 5600, Multimedia Systems and Applications VII, (25 October 2004); doi: 10.1117/12.571475
Show Author Affiliations
Helen Boussalis, California State Univ./Los Angeles (United States)
Charles Liu, California State Univ./Los Angeles (United States)
Khosrow Rad, California State Univ./Los Angeles (United States)
Jianyu Dong, California State Univ./Los Angeles (United States)


Published in SPIE Proceedings Vol. 5600:
Multimedia Systems and Applications VII
Chang Wen Chen; C.-C. Jay Kuo; Anthony Vetro, Editor(s)

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