基于多波段预测的高光谱图像分布式无损压缩

Distributed lossless compression of hyperspectral images based on multi-band prediction

提出了一种基于分布式信源编码的高光谱图像无损压缩算法,用于星载高光谱数据的有效压缩。为充分利用高光谱图像较强的谱间相关性,引入多波段谱间线性预测方案获取当前编码块的预测值,有效降低了编码块的最大预测残差值。在此基础上,根据最大预测残差值确定编码块各像素所属陪集的索引,通过传输每个像素所属陪集的索引代替预测残差,实现高光谱图像压缩。对星载可见/红外成像光谱仪(AVIRIS)获取的高光谱图像进行实验,并与已有的典型算法进行比较,结果显示该算法能够取得较好的无损压缩效果,同时具有较低的编码复杂度,适用于星载高光谱图像的无损压缩。

A lossless compression algorithm based on distributed source coding was proposed to compress the airborne hyperspectral data effectively.In order to make full use of the spectral correlation of hyperspectral images,a multi-band prediction scheme was introduced to acquire the prediction values of the current block and to reduce the maximal absolute value of prediction error effectively.Then,by using the maximal absolute value to determine the coset index of pixels belonging to the current block,the lossless compression of hyperspectral images was realized by transmitting the coset index of the current block instead of its prediction error.Experimental results on hyperspectral images acquired by Airborne Visible Infrared Imaging Spectrometer（AVIRIS） show that the proposed algorithm can offer both high compression performance and low encoder complexity compared with those existing classical algorithms,which is available for on-board compression of hyperspectral images.