Through the analysis of the cloud storage, cloud computing technology and characteristics of the remote sensing data, a method of mass remote sensing data stored in the cloud is put forward in this paper. Combined with the characteristics of remote sensing data and its processing algorithm, three program designing ideas, namely making data parallel, algorithm parallel and data algorithm nested parallel are proposed. By comparing the experimental results, realization of remote sensing data parallel processing based on cloud platform shows the superiority of the technical solution.
In this paper, a fractal-based compression approach of wavelet image is presented. The scheme tries to make full use of the sensitivity features of the human visual system. With the wavelet-based multi-resolution representation of image, detail vectors of each high frequency sub-image are constructed in accordance with its spatial orientation in order to grasp the edge information to which human observer is sensitive. Then a multi-level selection algorithm based on human vision's contrast masking effect is proposed to make the decision whether a detail vector is coded or not. Those vectors below the contrast threshold are discarded without introducing visual artifacts because of the ignorance of human vision. As for the redundancy of the retained vectors, different fractal- based methods are employed to decrease the correlation in single sub-image and between the different resolution sub- images with the same orientation. Experimental results suggest the efficiency of the proposed scheme. With the standard test image, our approach outperforms the EZW algorithm and the JPEG method.
An effective image management system should provide efficient storage of the image collection, while simultaneously providing fast content-based retrieval of the images. The traditional image management approach is to handle compression and indexing separately. Compression does not address the issue of image indexing.
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