Method for hue plane preserving color correction.
Method for hue plane preserving color correction.
复制标题
保持色调平面色彩校正的方法。
DOI：
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发表时间：
2016
影响因子：
1.9
通讯作者：
G. Finlayson
中科院分区：
文献类型：
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作者：
Michal Mackiewicz;C. F. Andersen;G. Finlayson
由Andersen和Hardeberg引入的色调平面保持颜色校正（HPPCC）[Proceedings of the 13th Color and Imaging Conference（CIC）（2005），pp. 141-146]，使用一组线性变换将依赖于设备的颜色值（RGB）映射到比色颜色值（XYZ），该线性变换由保留白色点的3×3矩阵实现，其中每个变换被学习并应用于由两个相邻色调平面定义的颜色空间的子区域中。将相机RGB值的色调平面界定的子区域映射到所估计的色度XYZ值的对应色调平面界定的子区域。色调平面是几何半平面，其中每个由中性轴和线性颜色空间中的彩色定义。HPPCC方法的主要优点是，在提供高阶方法的估计精度的同时，它保持色调平面中颜色的线性色度关系。作为一个重要的结果，它因此还使得色度估计对对象反射的曝光和阴影不变。在本文中，我们提出了一个新的灵活和强大的版本的HPPCC使用约束最小二乘法的优化，其中的子区域可以自由选择的数量和位置，以优化结果，同时约束变换连续性的子区域边界。该方法进行了比较，选择其他国家的最先进的表征方法，结果表明，它优于原来的HPPCC方法。
Hue plane preserving color correction (HPPCC), introduced by Andersen and Hardeberg [Proceedings of the 13th Color and Imaging Conference (CIC) (2005), pp. 141-146], maps device-dependent color values (RGB) to colorimetric color values (XYZ) using a set of linear transforms, realized by white point preserving 3×3 matrices, where each transform is learned and applied in a subregion of color space, defined by two adjacent hue planes. The hue plane delimited subregions of camera RGB values are mapped to corresponding hue plane delimited subregions of estimated colorimetric XYZ values. Hue planes are geometrical half-planes, where each is defined by the neutral axis and a chromatic color in a linear color space. The key advantage of the HPPCC method is that, while offering an estimation accuracy of higher order methods, it maintains the linear colorimetric relations of colors in hue planes. As a significant result, it therefore also renders the colorimetric estimates invariant to exposure and shading of object reflection. In this paper, we present a new flexible and robust version of HPPCC using constrained least squares in the optimization, where the subregions can be chosen freely in number and position in order to optimize the results while constraining transform continuity at the subregion boundaries. The method is compared to a selection of other state-of-the-art characterization methods, and the results show that it outperforms the original HPPCC method.