基于字典学习的卫星图像压缩算法研究.

To address the problem of satellite images in the transmission and storage, this paper designed a two-level lossless compress ion algorithm based on sparse representation for satellite images. It replaced the transmission of satellite images by that of sparse coefficients which was created by sparse representation, realizing the first-level compression. Firstly, it pre-processed the non-zero sparse coefficients to realize clustering, and sorted the locations of the original non-zero sparse coefficients by the clustering index. Then, utilizing the result of clustering, it divided the reordered sparse coefficients and the position data into blocks. Finally, it proposed an improved adaptive Huffman coding algorithm to code the blocks of sparse coefficients, while the blocks of their locations were via difference coding followed by improved Huffman coding, and the two-level compression of the image data was accordingly done. Experimental results show that the proposed algorithm is superior to the traditional algorithm, and the compression ratio of the improved algorithm is about 1/3 - 112 times that of the traditional algorithm, which can achieve high lossless compression and high resolution reconstruction of satellite images. [ABSTRACT FROM AUTHOR]