Lossless compression for aurora spectral images using fast online bi-dimensional decorrelation method

Wanqiu Kong; Jiaji Wu; Zejun Hu; Marco Anisetti; Ernesto Damiani; Gwanggil Jeon

doi:10.1016/j.ins.2016.11.008

Lossless compression for aurora spectral images using fast online bi-dimensional decorrelation method

Wanqiu Kong
, Jiaji Wu
, Zejun Hu
, Marco Anisetti
, Ernesto Damiani
, Gwanggil Jeon

Electrical Engineering

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

11 Scopus citations

Abstract

In this paper, we propose a lossless compression method to resolve the limitations in the real-time transmission of aurora spectral images. This method bi-dimensionally decorrelates the spatial and spectral domains and effectively removes side information of recursively computed coefficients to achieve high quality rapid compression. Experiments on data sets captured from the Antarctic Zhongshan Station show that the proposed algorithm can meet real-time requirements by using parallel processing to achieve outstanding compression ratio performance with low computational complexity.

Original language	British English
Pages (from-to)	33-45
Number of pages	13
Journal	Information Sciences
Volume	381
DOIs	https://doi.org/10.1016/j.ins.2016.11.008
State	Published - 1 Mar 2017

Keywords

Aurora spectral images
Bi-dimensional decorrelation
Lossless compression
Online
Parallel

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10.1016/j.ins.2016.11.008

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title = "Lossless compression for aurora spectral images using fast online bi-dimensional decorrelation method",

abstract = "In this paper, we propose a lossless compression method to resolve the limitations in the real-time transmission of aurora spectral images. This method bi-dimensionally decorrelates the spatial and spectral domains and effectively removes side information of recursively computed coefficients to achieve high quality rapid compression. Experiments on data sets captured from the Antarctic Zhongshan Station show that the proposed algorithm can meet real-time requirements by using parallel processing to achieve outstanding compression ratio performance with low computational complexity.",

keywords = "Aurora spectral images, Bi-dimensional decorrelation, Lossless compression, Online, Parallel",

author = "Wanqiu Kong and Jiaji Wu and Zejun Hu and Marco Anisetti and Ernesto Damiani and Gwanggil Jeon",

note = "Funding Information: This work is supported by National Natural Science Foundation of China under 61377011 and 41274164 , NRF of Korea under 2015R1D1A1A01058171, and the Chinese Meridian Project. Publisher Copyright: {\textcopyright} 2016 Elsevier Inc.",

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doi = "10.1016/j.ins.2016.11.008",

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T1 - Lossless compression for aurora spectral images using fast online bi-dimensional decorrelation method

AU - Kong, Wanqiu

AU - Wu, Jiaji

AU - Hu, Zejun

AU - Anisetti, Marco

AU - Damiani, Ernesto

AU - Jeon, Gwanggil

N1 - Funding Information: This work is supported by National Natural Science Foundation of China under 61377011 and 41274164 , NRF of Korea under 2015R1D1A1A01058171, and the Chinese Meridian Project. Publisher Copyright: © 2016 Elsevier Inc.

PY - 2017/3/1

Y1 - 2017/3/1

N2 - In this paper, we propose a lossless compression method to resolve the limitations in the real-time transmission of aurora spectral images. This method bi-dimensionally decorrelates the spatial and spectral domains and effectively removes side information of recursively computed coefficients to achieve high quality rapid compression. Experiments on data sets captured from the Antarctic Zhongshan Station show that the proposed algorithm can meet real-time requirements by using parallel processing to achieve outstanding compression ratio performance with low computational complexity.

AB - In this paper, we propose a lossless compression method to resolve the limitations in the real-time transmission of aurora spectral images. This method bi-dimensionally decorrelates the spatial and spectral domains and effectively removes side information of recursively computed coefficients to achieve high quality rapid compression. Experiments on data sets captured from the Antarctic Zhongshan Station show that the proposed algorithm can meet real-time requirements by using parallel processing to achieve outstanding compression ratio performance with low computational complexity.

KW - Aurora spectral images

KW - Bi-dimensional decorrelation

KW - Lossless compression

KW - Online

KW - Parallel

UR - https://www.scopus.com/pages/publications/84998636478

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VL - 381

SP - 33

EP - 45

JO - Information Sciences

JF - Information Sciences

ER -

Lossless compression for aurora spectral images using fast online bi-dimensional decorrelation method

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Keywords

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