Lossless hyperspectral-image compression using context-based conditional average

Hongqiang Wang; S. Derin Babacan; Khalid Sayood

doi:10.1109/TGRS.2007.906085

Lossless hyperspectral-image compression using context-based conditional average

Hongqiang Wang, S. Derin Babacan, Khalid Sayood

Electrical & Computer Engineering

Research output: Contribution to journal › Article › peer-review

59 Scopus citations

Abstract

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.

Original language	English (US)
Pages (from-to)	4187-4193
Number of pages	7
Journal	IEEE Transactions on Geoscience and Remote Sensing
Volume	45
Issue number	12
DOIs	https://doi.org/10.1109/TGRS.2007.906085
State	Published - Dec 2007

Keywords

Conditional average
Context coding
Correlation
Entropy code
Golomb-rice code
Hyperspectral image
Image coding

ASJC Scopus subject areas

Electrical and Electronic Engineering
Earth and Planetary Sciences(all)

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10.1109/TGRS.2007.906085

Cite this

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

abstract = "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",

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

note = "Funding Information: Manuscript received October 25, 2006; revised May 15, 2007. This work was supported in part by the NASA Goddard Space Flight Center under a grant. H. Wang and K. Sayood are with the Department of Electrical Engineering, University of Nebraska–Lincoln, Lincoln, NE 68503 USA (e-mail: hqwang@eecomm.unl.edu; ksayood@eecomm.unl.edu). S. D. Babacan is with the Department of Electrical Engineering, Northwestern University, Evanston, IL 60208-3118 USA (e-mail: sdb@ northwestern.edu). Digital Object Identifier 10.1109/TGRS.2007.906085",

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doi = "10.1109/TGRS.2007.906085",

language = "English (US)",

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journal = "IEEE Transactions on Geoscience and Remote Sensing",

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AU - Wang, Hongqiang

AU - Babacan, S. Derin

AU - Sayood, Khalid

N1 - Funding Information: Manuscript received October 25, 2006; revised May 15, 2007. This work was supported in part by the NASA Goddard Space Flight Center under a grant. H. Wang and K. Sayood are with the Department of Electrical Engineering, University of Nebraska–Lincoln, Lincoln, NE 68503 USA (e-mail: hqwang@eecomm.unl.edu; ksayood@eecomm.unl.edu). S. D. Babacan is with the Department of Electrical Engineering, Northwestern University, Evanston, IL 60208-3118 USA (e-mail: sdb@ northwestern.edu). Digital Object Identifier 10.1109/TGRS.2007.906085

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N2 - 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.

AB - 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.

KW - Conditional average

KW - Context coding

KW - Correlation

KW - Entropy code

KW - Golomb-rice code

KW - Hyperspectral image

KW - Image coding

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JF - IEEE Transactions on Geoscience and Remote Sensing

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ER -

Lossless hyperspectral-image compression using context-based conditional average

Abstract

Keywords

ASJC Scopus subject areas

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