Abstract
Along with the development of the spectral imaging technology, the precision of the hyper-spectral imagery becomes very high, and the size of the hyper-spectral imagery becomes very large. In order to solve the problem of the transmission and the storage, it is necessary to research the compression algorithm. The traditional prediction algorithm is adopted in the serial processing mode, and the processing time is long. In this paper, we improve the efficiency of the parallel prediction compression algorithm, to meet the needs of the rapid compression. We select bands along the direction of spectral or the direction of space, so that the hyper-spectral imagery can be divided into sub images. We number the sub images, then send them to different processing units. Each unit does compression tasks at the same time. This paper also compares the relationship between the processing unit number and the compression time. The experiment shows that, the parallel predictive compression algorithm can improve the efficiency of compression 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
This paper will focus on the technology and methods of hyper-spectral imagery parallel compression, and gives a parallel algorithm for hyper-spectral imagery compression based on prediction
Each group has N/n+2 elements, where N is the total number of bands and n is the number of processing units
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. That will speed up the computation, or that will expand the size of problems. 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. Traditional methods use serial processing mode to do the hyper-spectral imagery compression. We will achieve the goal of the parallel compression of the hyper-spectral imagery
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