DocumentCode
2707684
Title
Lossless hyperspectral image compression using context-based conditional averages
Author
Wang, Hongqiang ; Babacan, S. Derin ; Sayood, Khalid
Author_Institution
Dept. of Electr. Eng., Nebraska Univ., Lincoln, NE, USA
fYear
2005
fDate
29-31 March 2005
Firstpage
418
Lastpage
426
Abstract
In this paper, we propose a compression algorithm focused on the peculiarities of hyperspectral images. The spectral redundancy in hyperspectral images is exploited by using a context matching method driven by the correlation between adjacent bands of hyperspectral spectral images. The method compares favorably with recent proposed lossless compression algorithms in terms of compression, with significantly lower complexity.
Keywords
data compression; geophysics computing; global warming; image coding; image matching; redundancy; complexity; context matching method; context-based conditional averages; lossless hyperspectral image compression; spectral redundancy; Compression algorithms; Data compression; Humans; Hyperspectral imaging; Hyperspectral sensors; Image coding; NASA; Partitioning algorithms; Spatial resolution; Vector quantization;
fLanguage
English
Publisher
ieee
Conference_Titel
Data Compression Conference, 2005. Proceedings. DCC 2005
ISSN
1068-0314
Print_ISBN
0-7695-2309-9
Type
conf
DOI
10.1109/DCC.2005.51
Filename
1402203
Link To Document