基于L_∞最小搜索和陪集码的高光谱图像无损及近无损压缩

Lossless and Near-Lossless Compression of Hyperspectral Images Based on Search for L_∞ Minimum and Coset Coding

分布式信源编码(DSC)由于其较低的编码复杂度及较高的抗误码性被应用于高光谱图像压缩.在典型的基于陪集码的分布式高光谱图像无损压缩算法s-DSC(scalar coset DSC)框架下,本文指出最优的预测准则应为无穷范数最小,提出了基于L∞最小搜索的预测方法来逼近最优准则,并将框架推广到近无损压缩.实验表明,和原有的s-DSC相比,本文算法无损压缩的平均码率降低了大约0.25bpp,近无损性能也明显优于JPEG-LS,本文算法具有较低的计算复杂度、较高的压缩性能,且具有一定的抗误码能力,适用于星上压缩.

Distributed source coding（DSC） is applied to hyperspectral image compression due to its low complexity and error resilience.In the framework of typical scalar coset coding based distributed compression method（s-DSC）,it is pointed out in this paper that the infinity-norm minimization should be the best criterion for prediction,and a sub-optimal prediction method based on search for L∞ minimum is proposed to approach the criterion.In addition,the compression scheme is extended to near-lossless compression.The experimental results show that the lossless compression bitrate of the proposed method is reduced by about 0.25bpp compared to s-DSC and the near-lossless compression outperforms JPEG-LS significantly.Owing to the advantages of low complexity,high performance and error resilience,the proposed method is quite suitable for onboard compression.