Lossless compression of hyperspectral images based on lookup table and residual offset

HE Yankun; BAI Yujie

doi:10.7510/jgjs.issn.1001-3806.2014.05.014

In order to improve the compression ratio of the hyperspectral remote sensing images, a new lookup table(LUT) prediction method was proposed based on residual offset. In the first spectral band of the hyperspectral images, the prediction was conducted within the spectral band by the median prediction method of lossless compression standard. In other spectral bands, the prediction was conducted between the spectral bands. Firstly, the current prediction value was found through locally averaged interband scaling lookup table (LAIS-LUT) prediction method. Then, the specific five pixels around the current prediction value were compared with the corresponding five pixels around the current value. After the comparison, the offset was obtained. The offset was added to the prediction residual error. Finally, the prediction residual error will be coded with algorithm coding. Theoretical analysis and experimental verification show that the lossless compression ratio of the proposed method is increased by about 0.05 in National Aeronautics and Space Administration data and by about 0.07 in Chinese data. This result is helpful to improve the compression efficiency of hyperspectral images.

Lossless compression of hyperspectral images based on lookup table and residual offset

1. College of Communication Engineering, Xidian University, Xi'an 710072;

2. Department of Electronic Information, Northwestern Polytechnical University, Xi'an 710072, China

Received Date: 2013-12-11

Accepted Date: 2014-01-07

Available Online: 2014-09-25

Abstract: In order to improve the compression ratio of the hyperspectral remote sensing images, a new lookup table(LUT) prediction method was proposed based on residual offset. In the first spectral band of the hyperspectral images, the prediction was conducted within the spectral band by the median prediction method of lossless compression standard. In other spectral bands, the prediction was conducted between the spectral bands. Firstly, the current prediction value was found through locally averaged interband scaling lookup table (LAIS-LUT) prediction method. Then, the specific five pixels around the current prediction value were compared with the corresponding five pixels around the current value. After the comparison, the offset was obtained. The offset was added to the prediction residual error. Finally, the prediction residual error will be coded with algorithm coding. Theoretical analysis and experimental verification show that the lossless compression ratio of the proposed method is increased by about 0.05 in National Aeronautics and Space Administration data and by about 0.07 in Chinese data. This result is helpful to improve the compression efficiency of hyperspectral images.

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Reference (14)

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Lossless compression of hyperspectral images based on lookup table and residual offset

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通讯作者: 陈斌, bchen63@163.com

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