Lossless hyperspectral image compression using context-based conditional averages

Hongqiang Wang; S. Denn Babacan; Khalid Sayood

Lossless hyperspectral image compression using context-based conditional averages

Hongqiang Wang, S. Denn Babacan, Khalid Sayood

Electrical & Computer Engineering

Research output: Contribution to journal › Conference article › peer-review

17 Scopus citations

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.

Original language	English (US)
Pages (from-to)	418-426
Number of pages	9
Journal	Data Compression Conference Proceedings
State	Published - 2005
Event	DCC 2005: Data Compression Conference - Snowbird, UT, United States Duration: Mar 29 2005 → Mar 31 2005

ASJC Scopus subject areas

Computer Networks and Communications

Cite this

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title = "Lossless hyperspectral image compression using context-based conditional averages",

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.",

author = "Hongqiang Wang and Babacan, {S. Denn} and Khalid Sayood",

year = "2005",

language = "English (US)",

pages = "418--426",

journal = "Proceedings of the Data Compression Conference",

issn = "1068-0314",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

note = "DCC 2005: Data Compression Conference ; Conference date: 29-03-2005 Through 31-03-2005",

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AU - Wang, Hongqiang

AU - Babacan, S. Denn

AU - Sayood, Khalid

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AB - 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.

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M3 - Conference article

AN - SCOPUS:26944457361

SN - 1068-0314

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JO - Proceedings of the Data Compression Conference

JF - Proceedings of the Data Compression Conference

T2 - DCC 2005: Data Compression Conference

Y2 - 29 March 2005 through 31 March 2005

ER -

Lossless hyperspectral image compression using context-based conditional averages

Abstract

ASJC Scopus subject areas

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