| 英文标题 | Research on satellite image compression algorithm based on dictionary learning |
| 作者英文名 | Lu Guangyue, Zhai Jiaojiao, Li Shen |
| 机构英文名 | a.Shaanxi Key Laboratory of Information Communication Network & Security,b.College of Communication & Information Engineering,Xi'an University of Posts & Telecommunications,Xi'an 710121,China |
| 英文摘要 | To address the problem of satellite images in the transmission and storage, this paper designed a two-level lossless compression 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 1/3~1/2 times that of the traditional algorithm, which can achieve high lossless compression and high resolution reconstruction of satellite images. |
| 英文关键词 | image compression; dictionary learning; sparse representation; clustering; Huffman coding |