基于双向波段预测的超光谱图像无损压缩

Hyperspectral Image Lossless Compression Algorithm Based on Bidirectional Interband Prediction

为了解决超光谱图像海量数据无损压缩计算复杂度高、实时性差的问题,将预测树模型和双向多波段谱间预测算法用于超光谱无损压缩研究。在对超光谱图像进行基于预测树模型的谱内预测的基础上,通过双向谱间多波段预测,利用谱间局部统计冗余和结构冗余,建立了对预测树模型误差进行自适应补偿的预测器模型,设计了一种基于"权重"的方法。该方法利用已编码像素对系数进行自适应估计。采用SPIHT(Set Partitioning in Hierarchical Trees)算法对去冗余后的残差图像进行编码。试验结果表明,该算法在较低的计算复杂度下,压缩比优于目前流行的无损压缩算法。

To improve the real-time performance of the current compression algorithms on hyperspectral image, a new lossless compression method based on prediction tree with error variances compensated for hyperspeetral image is proposed. The method incorporates prediction tree and adaptive interband prediction techniques. The bi- directional interband prediction to current band is applied to hyperspectral image compression. The error created by prediction tree is compensated by linear adaptive predictor which deorrelates spectral statistic redundancy. In consideration of the complexity for the coefficients＇ calculation, a correlation-driven adaptive estimator is designed with which parameters are uniquely determined by the previously coded pixels. After de-correlating intraband and interband redundancy, an efficient wavelet coding method, SPIHT （Set Partitioning in Hierarchical Trees）, is used to encode residual image. The experiments show that the proposed method achieves both low overhead and high compression ratio in comparison with the popular lossless compression algorithm.