《计算机应用研究》|Application Research of Computers

基于字典学习的卫星图像压缩算法研究

Research on satellite image compression algorithm based on dictionary learning

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作者 卢光跃,翟皎皎,李珅
机构 西安邮电大学 a.陕西省信息通信网络与安全重点实验室;b.通信与信息工程学院,西安 710121
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文章编号 1001-3695(2020)12-058-3799-04
DOI 10.19734/j.issn.1001-3695.2019.07.0314
摘要 针对卫星图像的特点及当前卫星图像在传输和存储上面临的问题,提出了一种基于稀疏表示的卫星图像二级无损压缩算法。通过传输稀疏表示后的稀疏系数来代替图像本身的传输,完成对卫星图像的第一级压缩;对非零稀疏系数先作预处理后实现聚类,然后依据聚类索引对原始非零稀疏系数的位置排序;最后对处理后的非零稀疏系数和位置数据分块,并利用改进的自适应哈夫曼算法对非零稀疏系数的数据块编码,利用差分编码和改进的自适应哈夫曼算法对位置数据块编码,完成对图像数据的第二级压缩。实验结果表明,与传统算法相比,所提算法具有明显优势,改进算法的压缩率是传统算法的1/3~1/2,且可同时实现卫星图像的高倍无损压缩与高分辨率重建。
关键词 图像压缩; 字典学习; 稀疏表示; 聚类; 哈夫曼编码
基金项目 自然科学基础研究计划一般项目(2019JQ-377)
本文URL http://www.arocmag.com/article/01-2020-12-058.html
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
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收稿日期 2019/7/11
修回日期 2019/9/6
页码 3799-3802
中图分类号 TP751
文献标志码 A