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
data measured directly from the displays and corresponding
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
inputs and the resulting
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
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.
