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Proceedings Paper

Hyper-spectral image compression algorithm based on mixing transform of wave band grouping to eliminate redundancy
Author(s): ChengJun Xie; Lin Xu
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Paper Abstract

This paper presents an algorithm based on mixing transform of wave band grouping to eliminate spectral redundancy, the algorithm adapts to the relativity difference between different frequency spectrum images, and still it works well when the band number is not the power of 2. Using non-boundary extension CDF(2,2)DWT and subtraction mixing transform to eliminate spectral redundancy, employing CDF(2,2)DWT to eliminate spatial redundancy and SPIHT+CABAC for compression coding, the experiment shows that a satisfied lossless compression result can be achieved. Using hyper-spectral image Canal of American JPL laboratory as the data set for lossless compression test, when the band number is not the power of 2, lossless compression result of this compression algorithm is much better than the results acquired by JPEG-LS, WinZip, ARJ, DPCM, the research achievements of a research team of Chinese Academy of Sciences, Minimum Spanning Tree and Near Minimum Spanning Tree, on the average the compression ratio of this algorithm exceeds the above algorithms by 41%,37%,35%,29%,16%,10%,8% respectively; when the band number is the power of 2, for 128 frames of the image Canal, taking 8, 16 and 32 respectively as the number of one group for groupings based on different numbers, considering factors like compression storage complexity, the type of wave band and the compression effect, we suggest using 8 as the number of bands included in one group to achieve a better compression effect. The algorithm of this paper has priority in operation speed and hardware realization convenience.

Paper Details

Date Published: 5 March 2008
PDF: 9 pages
Proc. SPIE 6623, International Symposium on Photoelectronic Detection and Imaging 2007: Image Processing, 66231V (5 March 2008); doi: 10.1117/12.791531
Show Author Affiliations
ChengJun Xie, Beihua Univ. (China)
Lin Xu, Beihua Univ. (China)


Published in SPIE Proceedings Vol. 6623:
International Symposium on Photoelectronic Detection and Imaging 2007: Image Processing

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