论文标题
颜色到灰色投影操作员的均衡和亮度映射模式
Equalization and Brightness Mapping Modes of Color-to-Gray Projection Operators
论文作者
论文摘要
在本文中，将颜色RGB图像转换为灰度，涵盖了用于将3个颜色通道投射到单个颜色通道的数学运算符的表征。基于以下事实：大多数运营商将$ 256^3 $颜色的每个颜色分配给单个灰度，范围从0到255，他们正在聚集算法，这些算法将颜色总体分配到256个群集中，以增加亮度。为了可视化操作员的工作方式，绘制了群集的尺寸和每个集群的平均亮度。这项工作中引入的均衡模式（EQ）集中在集群大小上，而亮度映射（BM）模式描述了每个集群的CIE L*亮度分布。在线性运算符中发现了三类EQ模式和两类BM模式，定义了6级分类法。考虑到同等重量统一的操作员，NTSC标准运算符以及被选为理想的算法，以减轻黑人的面孔以改善当前有偏见的分类器中的面部识别，这是在案例研究中应用的理论/方法学框架。发现大多数用于评估颜色转换质量的当前指标更好地评估了两个BM模式类之一，但是人团队选择的理想操作员属于另一个类别。因此，该警告不要将这些通用指标用于特定目的颜色到灰色转换。应当指出的是，该框架对非线性操作员的最终应用可能会引起新的EQ和BM模式。本文的主要贡献是提供一种工具，以在当前更好的模型解释性趋势中更好地理解灰色转换器的颜色，甚至基于机器学习。
In this article, the conversion of color RGB images to grayscale is covered by characterizing the mathematical operators used to project 3 color channels to a single one. Based on the fact that most operators assign each of the $256^3$ colors a single gray level, ranging from 0 to 255, they are clustering algorithms that distribute the color population into 256 clusters of increasing brightness. To visualize the way operators work the sizes of the clusters and the average brightness of each cluster are plotted. The equalization mode (EQ) introduced in this work focuses on cluster sizes, while the brightness mapping (BM) mode describes the CIE L* luminance distribution per cluster. Three classes of EQ modes and two classes of BM modes were found in linear operators, defining a 6-class taxonomy. The theoretical/methodological framework introduced was applied in a case study considering the equal-weights uniform operator, the NTSC standard operator, and an operator chosen as ideal to lighten the faces of black people to improve facial recognition in current biased classifiers. It was found that most current metrics used to assess the quality of color-to-gray conversions better assess one of the two BM mode classes, but the ideal operator chosen by a human team belongs to the other class. Therefore, this cautions against using these general metrics for specific purpose color-to-gray conversions. It should be noted that eventual applications of this framework to non-linear operators can give rise to new classes of EQ and BM modes. The main contribution of this article is to provide a tool to better understand color to gray converters in general, even those based on machine learning, within the current trend of better explainability of models.
