针对基于单波段预测的高光谱图像无损压缩压缩比低的问题, 提出基于上下文窗口中反向搜索的高光谱图像无损压缩算法。首先, 对待测像素设定上下文窗口,计算其预测参考值并进行反向搜索预测得到待测像素的候选预测值。然后, 选取与预测参考值最接近的候选预测值作为待测像素的最终预测结果。最后, 对预测残差图像进行一阶算术编码完成压缩过程。利用提出的算法对AVIRIS 1997高光谱图像进行了实验, 结果显示, 提出的算法通过对上下文窗口、等效系数和有效像素阈值的优化取值, 使反向搜索预测的效果达到最好, 经过算术编码器编码后, 可以得到一个3.63倍的平均压缩比。 该方法具有较低的算法复杂度和内存需求, 优于当前已报道的基于单波段预测的其他各种高光谱图像无损压缩算法。

An efficient lossless compression scheme of hyperspectral images based on one-band-prediction was proposed by using backward search in a context window to improve the compression ratio of the hyperspectral images. Firstly, the context window for a pixel to be tested was defined and the prediction reference value under testing was calculated. Then,the candidate predictors which were mostly closed to the prediction reference value were selected as the final prediction results of the pixel to be measured. Finally, the predicted residual image was coded by a first-order arithmetic to implement the image compression. The method proposed was used in the experiments on hyperspectral images from Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) 1997, and the results show that the method has obtained the best prediction results of backward search by optimizing the context window, equivalent coefficients and effective pixel thresholds. After coding by an arithmetic coder, the average compression ratio is 3.63。 The method has a lower computing complexity and smaller memory requirements, and outperforms all other lossless compression schemes for hyperspectral images that have been previously reported.