【摘要】 针对传统递归最小二乘预测器的预测精度与谱间相关程度存在较强相关性及其对预测顺序较为敏感的特点,提出一种基于自适应波段选择和最佳预测顺序的高光谱图像无损压缩方法。首先,为了提高参考波段与待预测波段间的谱间相关性,以最大谱间相关系数为准则进行波段重排预处理,接着引入自适应波段选择策略从已预测波段集中选出与待预测波段存在最高相关性的多个波段作为参考波段。然后,以最小预测残差熵为准则选出最佳预测顺序模式进行谱间预测。最后,采用算术编码器对预测残差进行熵编码。在AVIRIS 2006数据集上的实验结果显示,该方法在16位校正图像、16位未校正图像和12位未校正图像上分别取得了3.314,5.594和2.395 bpp的压缩效果。该方法在几乎不增加计算复杂度的情况下有效提高了传统递归最小二乘预测器的预测精度,其最佳压缩效果接近或优于其他同类方法。更多还原

【Abstract】 The prediction accuracy of a Conventional Recursive Least Square(CRLS) predictor is strongly correlated with the inter-spectral correlation and is sensitive to the sequence in which the pixels are predicted. In view thereof, a lossless compression method for hyperspectral images was proposed. The method, which was based on the CRLS predictor, was modified to enable the selection of adaptive bands and to optimize the prediction sequence mode. First, to improve the correlation between the reference bands and the band to be predicted, the bands of the hyperspectral image were reordered according to the criterion of the maximum inter-spectral correlation coefficient in the preprocessing stage. Subsequently, the adaptive band selection strategy was used to select multiple bands with the highest correlation with the band to be predicted for use as prediction reference bands. Afterwards, the CRLS predictor with the best prediction sequence mode, selected by the minimum prediction residual entropy, was used for inter-spectral prediction. Finally, the arithmetic encoder was used to encode the prediction residual. Experiments on the AVIRIS 2006 dataset show that this method achieves bit rates of 3.314, 5.594, and 2.395 bpp on a 16-bit calibrated image, 16-bit uncalibrated image, and 12-bit uncalibrated image, respectively. These results indicate that this method can effectively improve the prediction accuracy of the CRLS predictor without significantly increasing the computational complexity. The best result of the proposed method closely approximates or is superior to that of other similar methods.更多还原