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Proceedings Paper

Fast DPCM scheme for lossless compression of aurora spectral images
Author(s): Wanqiu Kong; Jiaji Wu
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Paper Abstract

Aurora has abundant information to be stored. Aurora spectral image electronically preserves spectral information and visual observation of aurora during a period to be studied later. These images are helpful for the research of earth-solar activities and to understand the aurora phenomenon itself. However, the images are produced with a quite high sampling frequency, which leads to the challenging transmission load. In order to solve the problem, lossless compression turns out to be required.

Indeed, each frame of aurora spectral images differs from the classical natural image and also from the frame of hyperspectral image. Existing lossless compression algorithms are not quite applicable. On the other hand, the key of compression is to decorrelate between pixels. We consider exploiting a DPCM-based scheme for the lossless compression because DPCM is effective for decorrelation. Such scheme makes use of two-dimensional redundancy both in the spatial and spectral domain with a relatively low complexity. Besides, we also parallel it for a faster computation speed. All codes are implemented on a structure consists of nested for loops of which the outer and the inner loops are respectively designed for spectral and spatial decorrelation. And the parallel version is represented on CPU platform using different numbers of cores.

Experimental results show that compared to traditional lossless compression methods, the DPCM scheme has great advantage in compression gain and meets the requirement of real-time transmission. Besides, the parallel version has expected computation performance with a high CPU utilization.

Paper Details

Date Published: 24 October 2016
PDF: 6 pages
Proc. SPIE 10007, High-Performance Computing in Geoscience and Remote Sensing VI, 100070M (24 October 2016); doi: 10.1117/12.2241496
Show Author Affiliations
Wanqiu Kong, Xidian Univ. (China)
Jiaji Wu, Xidian Univ. (China)


Published in SPIE Proceedings Vol. 10007:
High-Performance Computing in Geoscience and Remote Sensing VI
Bormin Huang; Sebastián López; Zhensen Wu; Jose M. Nascimento; Jun Li; Valeriy V. Strotov, Editor(s)

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