A Color Mapping Automatic Tuning Framework for Complex Simulation Datasets

Abstract

Real-world simulations of physical effects generate complex data with extremely uneven numerical distributions and a large amount of background noise. Linear color mapping method to convert these data to color make it difficult to obtain visual results with clear physical features. A color mapping framework based on automatic tuning of color control points is proposed, including information-based removal of background data, distribution-based position tuning of color control points, and luminance-based color recognition enhancement. Three real-world simulation data, including detonation shock wave, electromagnetic environment, and high-power microwave, are used for the experimental tests in this paper. The results show the effectiveness of proposed framework, which can obtain visual results with high-quality detailed features, and the color recognition can be improved by an order of magnitude.