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Non-von-Kries 3-Parameter Color Prediction

机译:非von-kries 3参数颜色预测

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Chromatic adaptation transforms generally rely on a variant of the von Kries transformation method to account for changes in the LMS cone signals that occur when changing from one illuminant to another. Von Kries adaptation-also often referred to as the coefficient rule method or the diagonal transformation method-adjusts the 3 color channels by independent scale factors. Since there generally are only 3 known quantities available, namely the ratio of the cone signals of the two adapting illuminants, a crucial aspect of the von Kries method is that it requires only 3 parameters to be specified. A 9-parameter, 3x3 matrix transformation would be more accurate, but it is generally not possible to determine the extra parameters. This paper presents a novel method of predicting the effect a change of illumination has on the cone signals, while still relying on only 3 parameters. To begin, we create a large set of 3x3 matrices representing illuminant changes based on a sizable database of typical illuminant spectra and surface spectral reflectances. Representing these 3x3 matrices as points in a 9-dimensional space, we then apply principal components analysis to find a 3-dimensional basis which best approximates the original matrix space. To model an illumination change, a 3x3 matrix is constructed using a weighted combination of the 3 basis matrices. The relative weights can be calculated based on the 3 standard cone ratios obtained from the illuminant pair. Tests show that the new method yields better results than von Kries adaptation with or without sensor sharpening.
机译:色度适应通常依赖于von Kries变换方法的变型,以解释在从一个发光物到另一个发光时发生时发生的LMS锥体信号的变化。 von Kries适应 - 通常称为系数规则方法或对角线变换方法 - 通过独立的比例因子调整3个色频道。由于通常只有3个已知量,即两个适应光源的锥体信号的比率,Von Kries方法的一个关键方面是它只需要指定3个参数。一个9参数,3x3矩阵变换更准确,但通常无法确定额外的参数。本文提出了一种预测效果的新方法,效果在锥体信号上的变化,同时仍然仅依赖于3个参数。首先,我们创建了一组大量的3x3矩阵,表示基于典型光谱和表面光谱反射的可相同数据库的发光体变化。将这些3x3矩阵作为9维空间中的点,然后应用主成分分析以找到最佳近似于原始矩阵空间的三维基础。为了模拟照明变化,使用3个基质矩阵的加权组合构建3x3矩阵。可以基于从发光体对获得的3个标准锥比来计算相对重量。测试表明,新方法比Von Kries适应或没有传感器锐化产生更好的结果。

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