15 November 2017 Multispectral image compression algorithm based on spectral clustering and wavelet transform
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Proceedings Volume 10605, LIDAR Imaging Detection and Target Recognition 2017; 106051X (2017) https://doi.org/10.1117/12.2292014
Event: LIDAR Imaging Detection and Target Recognition 2017, 2017, Changchun, China
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.
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Rong Huang, Rong Huang, Weidong Qiao, Weidong Qiao, Jianfeng Yang, Jianfeng Yang, Hong Wang, Hong Wang, Bin Xue, Bin Xue, Jinyou Tao, Jinyou Tao, } "Multispectral image compression algorithm based on spectral clustering and wavelet transform", Proc. SPIE 10605, LIDAR Imaging Detection and Target Recognition 2017, 106051X (15 November 2017); doi: 10.1117/12.2292014; https://doi.org/10.1117/12.2292014
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