基于字典学习的卫星图像压缩算法研究

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。

Research on satellite image compression algorithm based on dictionary learning

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.