Low-complexity multispectral images compression algorithm based distributed compressive sensing

Longxu Jin; Jin Li; Min Zhang; Yinan Wu

doi:10.1109/ISCID.2013.149

Low-complexity multispectral images compression algorithm based distributed compressive sensing

Longxu Jin
, Jin Li
, Min Zhang
, Yinan Wu

CAS - Changchun Institute of Optics Fine Mechanics and Physics

Research output: Contribution to conference › Paper › peer-review

Abstract

In this paper, we proposes a low-complexity and excellent multispectral images compression algorithm based distributed compressive sensing. 2-D lifting discrete wavelet transform (DWT) is applied to eliminate spatial redundancy of each band of multispectral images. Unlike the traditional wavelet-based coders (e.g., CCSDS-IDC, etc), DWT coefficients of each band here are not directly encoded, but the high-frequency sub-bands are re-sampled by a fast compressive sensing (CS) measurements. Then the resultant CS measurements of each band are encoded by means of distributed source coding. Experimental results show that the proposed compression algorithm obtains better compression performance compared with the relevant existing algorithms.

Original language	English
Pages	142-145
Number of pages	4
DOIs	https://doi.org/10.1109/ISCID.2013.149
State	Published - 2013
Externally published	Yes
Event	6th International Symposium on Computational Intelligence and Design, ISCID 2013 - Hangzhou, China Duration: 28 Oct 2013 → 29 Oct 2013

Conference

Conference	6th International Symposium on Computational Intelligence and Design, ISCID 2013
Country/Territory	China
City	Hangzhou
Period	28/10/13 → 29/10/13

Keywords

Compressive sensing (CS)
Distributed source coding (DSC)
Multispectral image compression

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10.1109/ISCID.2013.149

Cite this

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title = "Low-complexity multispectral images compression algorithm based distributed compressive sensing",

abstract = "In this paper, we proposes a low-complexity and excellent multispectral images compression algorithm based distributed compressive sensing. 2-D lifting discrete wavelet transform (DWT) is applied to eliminate spatial redundancy of each band of multispectral images. Unlike the traditional wavelet-based coders (e.g., CCSDS-IDC, etc), DWT coefficients of each band here are not directly encoded, but the high-frequency sub-bands are re-sampled by a fast compressive sensing (CS) measurements. Then the resultant CS measurements of each band are encoded by means of distributed source coding. Experimental results show that the proposed compression algorithm obtains better compression performance compared with the relevant existing algorithms.",

keywords = "Compressive sensing (CS), Distributed source coding (DSC), Multispectral image compression",

author = "Longxu Jin and Jin Li and Min Zhang and Yinan Wu",

year = "2013",

doi = "10.1109/ISCID.2013.149",

language = "英语",

pages = "142--145",

note = "6th International Symposium on Computational Intelligence and Design, ISCID 2013 ; Conference date: 28-10-2013 Through 29-10-2013",

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AU - Jin, Longxu

AU - Li, Jin

AU - Zhang, Min

AU - Wu, Yinan

PY - 2013

Y1 - 2013

N2 - In this paper, we proposes a low-complexity and excellent multispectral images compression algorithm based distributed compressive sensing. 2-D lifting discrete wavelet transform (DWT) is applied to eliminate spatial redundancy of each band of multispectral images. Unlike the traditional wavelet-based coders (e.g., CCSDS-IDC, etc), DWT coefficients of each band here are not directly encoded, but the high-frequency sub-bands are re-sampled by a fast compressive sensing (CS) measurements. Then the resultant CS measurements of each band are encoded by means of distributed source coding. Experimental results show that the proposed compression algorithm obtains better compression performance compared with the relevant existing algorithms.

AB - In this paper, we proposes a low-complexity and excellent multispectral images compression algorithm based distributed compressive sensing. 2-D lifting discrete wavelet transform (DWT) is applied to eliminate spatial redundancy of each band of multispectral images. Unlike the traditional wavelet-based coders (e.g., CCSDS-IDC, etc), DWT coefficients of each band here are not directly encoded, but the high-frequency sub-bands are re-sampled by a fast compressive sensing (CS) measurements. Then the resultant CS measurements of each band are encoded by means of distributed source coding. Experimental results show that the proposed compression algorithm obtains better compression performance compared with the relevant existing algorithms.

KW - Compressive sensing (CS)

KW - Distributed source coding (DSC)

KW - Multispectral image compression

UR - https://www.scopus.com/pages/publications/84901024624

U2 - 10.1109/ISCID.2013.149

DO - 10.1109/ISCID.2013.149

M3 - 论文

AN - SCOPUS:84901024624

SP - 142

EP - 145

T2 - 6th International Symposium on Computational Intelligence and Design, ISCID 2013

Y2 - 28 October 2013 through 29 October 2013

ER -

Low-complexity multispectral images compression algorithm based distributed compressive sensing

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Keywords

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