Deep metric learning for color differences

Abstract

Numerous attempts have been made to define a color space and a color distance metric that would closely resemble human color vision. The main problem is that the human vision system is more sensitive to some colors, while less sensitive to others. Moreover, all colors are not even distinguishable by the human eyes. A distance given by an ideal metric would match the color difference seen by the human vision system. The idea behind this research is to define such a metric by utilizing the spectral data and the available information on distinguishable colors. Metric learning is performed by using deep neural networks. Those networks are also used to project spectral data onto a new color space. The resulting metric is then tested against the standard CIEDE2000 metric. The results indicate that the new color space with the metric is more perceptually uniform than the standard color space and metric. The new metric can then be used for better understanding of the human visual system and measuring the color differences.