Color-difference evaluation for digital images using a categorical judgment method
Color-difference evaluation for digital images using a categorical judgment method
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使用分类判断方法评估数字图像的色差
DOI：
10.1364/josaa.30.000616
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发表时间：
2013-04-01
影响因子：
1.9
通讯作者：
Melgosa, Manuel
中科院分区：
文献类型：
--
作者：
Liu, Haoxue;Huang, Min;Melgosa, Manuel
对8张ISO SCID自然图像中的5张图像的像素的CIELAB明度和色度值进行修改以生成样本图像。将成对的图像显示在校准过的显示器上，由12名具有正常色觉的观察者组成的小组使用分类判断法进行评估。实验结果表明，在假设明度参数因子k(L)=1来预测图像中的色差时，CIELAB的表现优于CIEDE2000、CIE94或CMC，这与针对均匀颜色对的色差文献中的结果不同。然而，观察者对图像中CIELAB明度和色度差异的感知方式不同。为了拟合当前的实验数据，提出了一种特定的方法来优化色差公式CIELAB、CIEDE2000、CIE94和CMC中的k(L)。从标准化残差平方和（STRESS）指数来看，发现优化后的公式CIEDE2000(2.3:1)、CIE94(3.0:1)和CMC(3.4:1)的表现明显优于其对应的明度参数因子k(L)=1的原始形式。具体而言，CIEDE2000(2.3:1)表现最佳，其平均STRESS值为25.8，令人满意，这与在该公式开发过程中使用的均匀颜色样本的组合加权数据集的CIEDE2000(1:1)公式得出的27.5的值非常相似[《美国光学学会会刊A》25卷，1828页（2008年），表2]。然而，根据我们的实验数据拟合，CIELAB(1.5:1)、CIEDE2000(2.3:1)、CIE94(3.0:1)和CMC(3.4:1)这四个优化公式中没有一个明显优于其他公式。目前的结果大致与国际照明委员会（CIE）最近的建议相符，即图像中的色差可以通过在CIELAB或CIEDE2000中简单地采用明度参数因子k(L)=2来预测[CIE出版物199:2011]。还发现这五张图像的不同内容对所测试的色差公式的性能有相当大的影响。（C）2013美国光学学会
The CIELAB lightness and chroma values of pixels in five of the eight ISO SCID natural images were modified to produce sample images. Pairs of images were displayed on a calibrated monitor and assessed by a panel of 12 observers with normal color vision using a categorical judgment method. The experimental results showed that assuming the lightness parametric factor k(L) = 1 to predict color differences in images, CIELAB performed better than CIEDE2000, CIE94, or CMC, which is a different result to the one found in color-difference literature for homogeneous color pairs. However, observers perceived CIELAB lightness and chroma differences in images in different ways. To fit current experimental data, a specific methodology is proposed to optimize k(L) in the color-difference formulas CIELAB, CIEDE2000, CIE94, and CMC. From the standardized residual sum of squares (STRESS) index, it was found that the optimized formulas, CIEDE2000(2.3:1), CIE94(3.0:1), and CMC(3.4:1), performed significantly better than their corresponding original forms with lightness parametric factor k(L) = 1. Specifically, CIEDE2000(2.3:1) performed the best, with a satisfactory average STRESS value of 25.8, which is very similar to the 27.5 value that was found from the CIEDE2000(1:1) formula for the combined weighted dataset of homogeneous color samples employed at the development of this formula [J. Opt. Soc. Am. A 25, 1828 (2008), Table 2]. However, fitting our experimental data, none of the four optimized formulas CIELAB(1.5:1), CIEDE2000 (2.3:1), CIE94(3.0:1), and CMC(3.4:1) is significantly better than the others. Current results roughly agree with the recent CIE recommendation that color difference in images can be predicted by simply adopting a lightness parametric factor k(L) = 2 in CIELAB or CIEDE2000 [CIE Publication 199:2011]. It was also found that the different contents of the five images have considerable influence on the performance of the tested color-difference formulas. (C) 2013 Optical Society of America