超光谱图像的分布式压缩

针对超光谱图像压缩进行了研究,提出了一种有效的基于分布式信源编码(Distributed Source Coding, DSC)的有损压缩算法。该算法利用多元陪集码和标量量化的方式实现超光谱图像的分布式有损压缩,针对分布式信源编码,利用多波段预测的方式为每个编码块构造边信息,然后采用标量量化的方式对编码块和其边信息同时进行量化处理。根据分布式信源编码原理,给出了各编码块量化后的编码码率。为了减少标量量化带来的信息丢失,算法引入了跳跃策越。部分均方误差意义上损失较大的编码块将由其边信息直接代替。实验结果表明,所提出的算法性能与基于小波变换的算法性能相当;此外,该算法复杂度较低,适合星载超光谱图像的压缩。

Distributed compression for hyperspectral i mages

An efficient lossy compression algorithm was presented based on distributed source coding. The proposed algorithm employed multilevel coset codes to perform distributed source coding and a block-based scalar quantizer to perform lossy compression. Multi-bands prediction was used to construct the side information of each block, and the scalar quantization was performed on each block and its side information simultaneously. According to the principles of distributed source coding, the bit-rate of each block after scalar quantization was given. To reduce the distortion introduced by scalar quantization, skip strategy was employed for those blocks that containing high distortion in the sense of mean squared errors introduced by scalar quantization, and the block was directly replaced by its side information. Experimental results show that the performance of the proposed algorithm is competitive with that of transform-based algorithms. Moreover, the proposed algorithm has low complexity which is suitable for onboard compression of hyperspectral images.