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Lossless hyperspectral-image compression using context-based conditional average
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DOI：10.1109/TGRS.2007.906085.png)
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摘要
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In this paper, a new algorithm for lossless compression of hyperspectral images is proposed. The spectral redundancy in hyperspectral images is exploited using a context-match method driven by the correlation between adjacent bands. This method is suitable for hyperspectral images in the band-sequential format. Moreover, this method compares favorably with the recent proposed lossless compression algorithms in terms of compression, with a lower complexity.
Keyword:
conditional average
context coding
correlation
entropy code
Golomb-Rice code
hyperspectral image
image coding
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期刊
IF:
8.6
论文数:
2.2W
被引数:
10.7W
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