Abstract
In this paper, we propose a lossless compression algorithm for hyper-spectral images with the help of the K-Means clustering and parallel prediction. We use K-Means clustering algorithm to classify hyper-spectral images, and we obtain a number of two dimensional sub images. We use the adaptive prediction compression algorithm based on the absolute ratio to compress the two dimensional sub images. The traditional prediction algorithm is adopted in the serial processing mode, and the processing time is long. So we improve the efficiency of the parallel prediction compression algorithm, to meet the needs of the rapid compression. In this paper, a variety of hyper-spectral image compression algorithms are compared with the proposed method. The experimental results show that the proposed algorithm can effectively improve the compression ratio of hyper-spectral images and reduce the compression time effectively.
Highlights
The parallel is a kind of way to improve the processing efficiency, and it uses multiple processing units to deal with the problem
Lena Chang proposed a parallel compression method based on group and region in 2011
Through the two order linear parallel prediction compression algorithm, we can speed up the efficiency of the hyper-spectral image compression
Summary
The parallel is a kind of way to improve the processing efficiency, and it uses multiple processing units to deal with the problem. The parallel computing needs parallel data processors, which can divide an application into multiple sub tasks. They are sent to different processors, and processors work together to accomplish tasks. Because the large size of the hyper-spectral imagery, the traditional compression method is hard to meet the requirements of the high-speed encoding and decoding. Lena Chang proposed a parallel compression method based on group and region in 2011. It contains two algorithms, which are clustering signal subspace projection (CSSP) and the maximum correlation band clus-tering (MCBC). Based on the two order linear prediction compression algorithm, we use K-Means clustering algorithm to divide the original hyper-spectral image to sub image. According to the correlation between the bands of the sub image, we design adaptive predictive compression algorithm, which can provide the basis for the parallel compression of hyper-spectral image
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