基于降低映射预测残差的高光谱图像压缩算法

CCSDS 123.0-B-1算法是空间数据系统咨询委员会为多/高光谱图像提出的自适应三维预测无损压缩标准,针对CCSDS 123.0-B-1算法中存在的未充分利用像素位置信息及谱间相关性、压缩率有待提高的问题,对该算法的预测器进行了优化,提出了RMPR算法。RMPR算法根据当前像元具体位置对预测点进行自适应选择,采用双向线性预测去除高光谱图像的谱间相关性,并使用优化的残差映射器提高预测精度、缩短压缩码长。利用10幅高光谱图像进行测试,结果表明,在保证无损压缩且压缩效率无显著差异的前提下,RMPR算法的压缩性能显著优于原算法。

Hyperspectral image compression through reducing mapping prediction residuals

CCSDS 123.0-B-1 algorithm is an adaptive lossless compression standard for multispectral and hyperspectral images proposed by the Consultative for Space Data Systems. In order to solve the problems existing in CCSDS 123.0-B-1 algorithm, such as the under-utilization of pixel position information and spectral correlation, and the compression rate to be improved, an RMPR (Reduction of Mapped Prediction Residual) algorithm is proposed to optimize the predictor of the algorithm. The RMPR algorithm can adaptively select the prediction points according to the specific position of the current pixel, remove the spectral correlation of hyperspectral images with bidirectional linear prediction, and use the optimized residual mapper to improve the prediction accuracy and shorten the compression code length. Test on ten hyperspectral images shows that the RMPR algorithm significantly outperforms the original algorithm in terms of compression performance, under the premise of lossless compression and no signi- ficant difference in compression efficiency.