Image-Difference Prediction: From Color to Spectral
Image-Difference Prediction: From Color to Spectral
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DOI：
10.1109/tip.2014.2311373
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发表时间：
2014-03
影响因子：
10.6
通讯作者：
S. L. Moan;P. Urban
中科院分区：
文献类型：
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作者：
S. L. Moan;P. Urban
我们提出了一种新的策略来评估多和高光谱图像的质量，从人类感知的角度。我们将光谱图像差异定义为在一组指定的观看条件（光源）下两个光谱图像之间的总体感知差异。首先，我们分析了稳定性的七个图像差异功能跨光源，通过信息理论的策略。我们证明，特别是，在常见的光谱失真（光谱色域映射，光谱压缩，光谱重建）的情况下，彩色功能的变化远大于消色差的，尽管考虑彩色适应。然后，我们提出了两个计算效率的光谱图像差异度量，并将它们与主观视觉实验的结果进行比较。一个显着的改善，如广泛使用的均方根误差现有的指标。
We propose a new strategy to evaluate the quality of multi and hyperspectral images, from the perspective of human perception. We define the spectral image difference as the overall perceived difference between two spectral images under a set of specified viewing conditions (illuminants). First, we analyze the stability of seven image-difference features across illuminants, by means of an information-theoretic strategy. We demonstrate, in particular, that in the case of common spectral distortions (spectral gamut mapping, spectral compression, spectral reconstruction), chromatic features vary much more than achromatic ones despite considering chromatic adaptation. Then, we propose two computationally efficient spectral image difference metrics and compare them to the results of a subjective visual experiment. A significant improvement is shown over existing metrics such as the widely used root-mean square error.