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Novel Approach to Uniformization of a Color Space via Generic Deep Learning-Based Transformation

Creation of a convenient uniform color space which will match experimental data and will be easy-to-use - one of a primary objectives of color science. In this work we present novel approach which transform any existing color space in order to make the discrimination ellipses unit circles. This allows to find color difference in a new color space as Euclidian distance with constant unit threshold. A priori, color difference in a new color space should match perceptual color difference, since transformation is based on experimental data. The lack of experimental data for every point may be overcame via deep learning-based regression model, namely, multilayer perceptron which takes color coordinates as input and returns ellipses’ parameters as output. Results are demonstrated on MacAdam’s ellipses in xy Chromaticity Diagram and Luo and Rigg chromaticity-discrimination ellipses in a*b* chromaticity plane. Additionally, topic of fitting discrimination ellipses is covered.