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Proceedings Paper • new

Multispectral image compression algorithm based on spectral clustering and wavelet transform
Author(s): Rong Huang; Weidong Qiao; Jianfeng Yang; Hong Wang; Bin Xue; Jinyou Tao
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Paper Abstract

In this paper, a method based on spectral clustering and the discrete wavelet transform (DWT) is proposed, which is based on the problem of the high degree of space-time redundancy in the current multispectral image compression algorithm. First, the spectral images are grouped by spectral clustering methods, and the clusters of similar heights are grouped together to remove the redundancy of the spectra. Then, wavelet transform and coding of the class representative are performed, and the space redundancy is eliminated, and the difference composition is applied to the Karhunen-Loeve transform (KLT) and wavelet transform. Experimental results show that with JPEG2000 and upon KLT + DWT algorithm, compared with the method has better peak signal-to-noise ratio and compression ratio, and it is suitable for compression of different spectral bands.

Paper Details

Date Published: 15 November 2017
PDF: 8 pages
Proc. SPIE 10605, LIDAR Imaging Detection and Target Recognition 2017, 106051X (15 November 2017); doi: 10.1117/12.2292014
Show Author Affiliations
Rong Huang, Xi'an Institute of Optics and Precision Mechanics (China)
Univ. of Chinese Academy of Sciences (China)
Weidong Qiao, Univ. of Chinese Academy of Sciences (China)
Jianfeng Yang, Univ. of Chinese Academy of Sciences (China)
Hong Wang, Univ. of Chinese Academy of Sciences (China)
Bin Xue, Univ. of Chinese Academy of Sciences (China)
Jinyou Tao, Univ. of Chinese Academy of Sciences (China)


Published in SPIE Proceedings Vol. 10605:
LIDAR Imaging Detection and Target Recognition 2017
Yueguang Lv; Weimin Bao; Weibiao Chen; Zelin Shi; Jianzhong Su; Jindong Fei; Wei Gong; Shensheng Han; Weiqi Jin; Jian Yang, Editor(s)

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