Scalable object-based compression algorithm for segmented space-telescope images

Helen Boussalis; Charles Liu; Khosrow Rad; Jianyu Dong

doi:10.1117/12.571475

25 October 2004 Scalable object-based compression algorithm for segmented space-telescope images

Helen Boussalis, Charles Liu, Khosrow Rad, Jianyu Dong

Proceedings Volume 5600, Multimedia Systems and Applications VII; (2004) https://doi.org/10.1117/12.571475
Event: Optics East, 2004, Philadelphia, Pennsylvania, United States

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.

Citation Download Citation

Helen Boussalis, Charles Liu, Khosrow Rad, and Jianyu Dong "Scalable object-based compression algorithm for segmented space-telescope images", Proc. SPIE 5600, Multimedia Systems and Applications VII, (25 October 2004); https://doi.org/10.1117/12.571475

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