Equalization and Brightness Mapping Modes of Color-to-Gray Projection
Operators
- URL: http://arxiv.org/abs/2208.09950v1
- Date: Sun, 21 Aug 2022 19:23:06 GMT
- Title: Equalization and Brightness Mapping Modes of Color-to-Gray Projection
Operators
- Authors: Diego Frias
- Abstract summary: 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.
Three classes of EQ modes and two classes of BM modes were found in linear operators, defining a 6-class taxonomy.
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.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
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