基于局部边缘预测的空谱联合高光谱图像无损压缩

Spatial-spectral lossless compression of hyperspectral images using local edge based prediction

利用高光谱图像丰富的边缘特性和很强的谱间结构相似性,提出一种基于局部边缘预测的空谱联合高光谱图像无损压缩方法。该方法利用谱间最小方差算法的编码框架,在原有谱内、谱间预测模式的基础上,增加了第三种"无预测"的预测模式,以更好地适应高光谱图像的相关特性。在谱内预测时,针对图像中普遍存在的局部斜边缘,将对角边缘检测引入到中值预测中,提出了改进的对角边缘预测算法。在谱间预测时,通过分析局部边缘存在时上下文的特点,提出简单有效的上下文选择策略,在此基础上,提出了基于局部边缘结构相似性的谱间预测算法,在上下文模板内自适应地选择最佳预测上下文进行谱间预测。实验结果表明,本文方法有效利用了图像的局部边缘特性,更好地去除了谱内和谱间的相关性,改善了预测性能,提高了无损压缩比。

By fully exploiting the abundant edge features and the strong interband structural correlation of hyperspectral images, a spatial-spectral lossless compression algorithm for hyperspectral images is proposed using local edge based prediction. Based on the coding framework of spectral oriented least square （SLSQ）, the algorithm presents a three-modes predictor, which adds a third prediction mode （no prediction） in addition to the original intraband prediction and interband prediction modes. Therefore, the proposed algorithm is more accordant with the correlation property of hyperspectral images. Considering that local diagonal edges generally exist in images, an improved diagonal edge based predictor is adopted for intraband prediction by introducing diagonal edge detection into the median predictor. For interband prediction, a simple but effective strategy for selecting the prediction context is first presented through the analysis of the property of the context when an edge exists in a local context window, followed by an interband predictor based on local edge structural similarity is used to select the optimal prediction context adaptively within the context window. Experimental results show that the proposed algorithm can better remove both intraband and interband correlations, improve the prediction performance and lossless compression ratio.