A comparative analysis of machine learning methods for display characterization

• Published in: Displays Vol. 85; p. 102849
• Main Authors: Almutairi, Khleef, Morillas, Samuel, Latorre-Carmona, Pedro, Dansoko, Makan, Gacto, María José
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.12.2024
• Subjects: Colorimetric measurements; Display characterization; Fuzzy inference systems; Machine learning; Regression models; Regression models; Display characterization; Colorimetric measurements; Machine learning; Fuzzy inference systems
• ISSN: 0141-9382
• DOI: 10.1016/j.displa.2024.102849

This paper explores the application of various machine-learning methods for characterizing displays of technologies LCD, OLED, and QLED to achieve accurate color reproduction. These models are formed from input (device-dependent RGB data) and output (device-independent XYZ coordinates) data obtained from three different displays. Training and test datasets are built using RGB data measured directly from the displays and corresponding XYZ coordinates measured with a high-precision colorimeter. A key aspect of this research is the application fuzzy inference systems for building interpretable models. These models offer the advantage of not only achieving good performance in color reproduction, but also providing physical insights into the relationships between the RGB inputs and the resulting XYZ outputs. This interpretability allows for a deeper understanding of the display’s behavior. Furthermore, we compare the performance of fuzzy models with other popular machine-learning approaches, including those based on neural networks and decision trees. By evaluating the strengths and weaknesses of each method, we aim to identify the most effective approach for display characterization. The effectiveness of each method is assessed by its ability to accurately reproduce and display colors, as measured by the ΔE00 visual error metric. Our findings indicate that the Fuzzy Modeling and Identification (FMID) method is particularly effective, allowing for an optimal balance between high accuracy and interpretability. Its competitive performance across all display types, combined with its valuable interpretability, provides insights for potential future calibration and optimization strategies. The results will shed light on whether machine learning methods offer an advantage over traditional physical models, particularly in scenarios with limited data. Additionally, the study will contribute to the understanding of the interpretability benefits offered by fuzzy inference systems in the context of LCD display characterization. •The study compares machine learning methods for characterizing LCD, OLED, and QLED.•High-precision colorimetric data used to build and test models for reliable results.•The effectiveness of each method is evaluated using the ΔE00 visual error metric for color accuracy.•Fuzzy FMID balance accuracy and interpretability in color reproduction.•Fuzzy inference systems provide deeper insights into RGB-XYZ relationships