基于双估计值的查找表高光谱图像无损压缩

提出一种基于双估计值的查找表预测高光谱图像无损压缩算法。首先,在高光谱图像的第1谱段图像采用JPEG-LS中值预测器进行谱段内预测,其他谱段图像采用谱间预测。谱间预测采用以下步骤,利用3个LUT预测值求出第一个估计值;其次用当前谱段内和前一谱段内特定的8个像素点计算出第二个估计值,将谱段内预测和谱间预测有效地结合,去除了高光谱图像的谱间相关性。然后,用3个LUT预测值和最终的预测估计值比较,选出最终的预测值。最后,将预测残差进行算术编码。实验结果表明,针对NASA的AVIRIS高光谱图像,用本文算法比LAIS-LUT的压缩比平均提高了0.03~0.11,针对国内OIMS-I高光谱图像,比LAIS-LUT压缩比平均提高了0.01~0.09,有效的提高了压缩比。

Lossless compression of hyperspectral images based on lookup table and two estimated values

A lossless compression algorithm of hyperspectral images based on two estimated value and look-up-table (LUT)prediction is proposed. Firstly,the intraband is predicted by using a JPEG-LS median predictor only for the first image along the spectral line,and the interband is predicted for the other bands. For interband prediction,the first estimated value is gotten by using three LUT prediction values,and then the second estimated value is computed by using eight specific pixels which located in the previous band and in the current band,the intraband prediction is effectively combined with the interband prediction,the spectrum correlation of hyperspectral images is removed. Then,the final estimated value is gotten through comparing the three LUT prediction values and the two estimated values. Lastly,the prediction error will be compressed by arithmetic coding. The experiment results show that:compared with LAIS-LUT,the proposed algorithm could enhance the compression ratio by about 0.03~0.11 for AVIRIS hyperspectral images data,and 0.01~0.09 for OIMS-I hyperspectral images,which improves the compression ratio effectively.