Perceptual Color Difference Modeling Using Machine Learning and Human Similarity Judgments

2026-09-30

AI Summary

This study addresses the misalignment between existing color difference metrics and human perception by training regression models on human similarity judgment data to predict perceived color differences. We propose COLIBRI features, which integrate numerical coordinates with fuzzy linguistic categories, and evaluate multiple machine learning algorithms. Our findings indicate that color representation features are more critical than algorithm selection. Specifically, a LightGBM model incorporating the proposed feature representation achieves an R² of 0.703, significantly outperforming conventional RGB and HSI methods. This approach effectively enhances both the accuracy and perceptual consistency of color difference assessment.

Abstract

Accurate assessment of color differences is essential for applications ranging from digital design to quality control. While existing color difference metrics, such as CIEDE2000, aim to approximate human perception, they may still exhibit inconsistencies with perceptual judgments. In this study, we investigate a data-driven approach to color-difference estimation based directly on human evaluations. We collect similarity judgments for 2,000 systematically generated color pairs, each rated by seven observers using a four-point ordinal scale. These judgments are then used to train regression models using different color representations, including RGB channel differences, HSI differences, and COLIBRI fuzzy linguistic categories. Experiments with five regression algorithms show that the choice of color model has a greater influence on prediction performance than the choice of regression algorithm. Using COLIBRI features alone, linear regression achieves an R2 of 0.595, outperforming RGB and HSI representations, which achieve R2 values of 0.479 and 0.493, respectively. The best performance is obtained by LightGBM using the combined representation, reaching an R2 of 0.703. The results indicate that human perceptual color differences are better captured when numerical color coordinates are complemented by graded perceptual categories, highlighting the potential of data-driven models for perceptually aligned color-difference estimation.

Problem

• perceptual color difference
• color difference metrics
• human similarity judgments
• human perception

Innovation

• Perceptual Color Difference
• Machine Learning
• COLIBRI Fuzzy Categories
• Data-driven Modeling
• Human Similarity Judgments