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Lossless hyperspectral-image compression using context-based conditional average

delete2007-12-01
delete49
PRE
AI
H
Hongqiang Wang *
S
S. Derin Babacan
K
Khalid Sayood
DOI：10.1109/TGRS.2007.906085delete
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Abstract

Abstract

En 中文
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.
Keywords:
conditional average
context coding
correlation
entropy code
Golomb-Rice code
hyperspectral image
image coding
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Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.

Journal

IEEE Transactions on Geoscience and Remote Sensing cover
IEEE Transactions on Geoscience and Remote Sensing
IF:
8.6
Papers:
2.1W
Citations:
10.7W

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No organization information available