基于混合预测高光谱图像无损压缩

为了解决海量遥感高光谱图像数据给有限存储空间和传输带宽带来的压力，利用高光谱图像具有较强谱间相关性的特点，提出了一种基于混合预测的高光谱图像无损压缩算法。第一谱段采用中值滤波器模型进行谱内预测；对于其他谱段采用线性预测与上下文预测相结合的谱间混合预测，线性预测是通过二阶线性预测器模型得到预测参考值，上下文预测是根据预测参考值利用上下文预测模型预测得到最终预测值；将预测得到的残差图像进行霍夫曼编码。对AVIRIS标准高光谱遥感图像进行无损压缩实验结果表明：利用该方法能够取得较好的无损压缩效果，平均压缩比达到3.17，与现有的无损压缩方法3D-CALIC、LUT、C-DPCM、JPEG-LS相比，平均压缩比提高了0.05~0.48，是一种高效的高光谱图像无损压缩方法。

Lossless Compression of Hyperspectral Images Using Hybrid Prediction

To solve the transmission and storage problems resulting from massive hyperspectral remote sensing data,us-ing the significant spectral correlation within the hyperspectral images, a lossless compression scheme based on hybrid prediction is proposed. Intra-band prediction is used only for the first image along the spectral line using a median pre-dictor. And inter-band hybrid prediction which is the combination of a linear prediction and a context prediction is ap-plied to other band images. Second order linear predictor is used to obtain a prediction reference value. The final predic-tion is obtained through context prediction model according to the prediction reference value. The residual image of hy-brid prediction is coded by the Huffman coding. Experimental results on AVIRIS hyperspectral images show that the proposed algorithm achieves better lossless compression, the average compression ratio is 3.17, which is 0.05~0.48 higher than those from other lossless compression algorithms such as 3D-CALIC, LUT, C-DPCM, JPEG-LS. It is an effective lossless compression method for hyperspectral images.