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王晓华, 曹轶, 王华维, 艾志玮, 张哲, 吴付坤. 面向效应场数值模拟的颜色映射自动调优框架[J]. 计算机辅助设计与图形学学报, 2022, 34(4): 623-635. DOI: 10.3724/SP.J.1089.2022.18946
引用本文: 王晓华, 曹轶, 王华维, 艾志玮, 张哲, 吴付坤. 面向效应场数值模拟的颜色映射自动调优框架[J]. 计算机辅助设计与图形学学报, 2022, 34(4): 623-635. DOI: 10.3724/SP.J.1089.2022.18946
Wang Xiaohua, Cao Yi, Wang Huawei, Ai Zhiwei, Zhang Zhe, Wu Fukun. A Color Mapping Automatic Tuning Framework for Complex Simulation Datasets[J]. Journal of Computer-Aided Design & Computer Graphics, 2022, 34(4): 623-635. DOI: 10.3724/SP.J.1089.2022.18946
Citation: Wang Xiaohua, Cao Yi, Wang Huawei, Ai Zhiwei, Zhang Zhe, Wu Fukun. A Color Mapping Automatic Tuning Framework for Complex Simulation Datasets[J]. Journal of Computer-Aided Design & Computer Graphics, 2022, 34(4): 623-635. DOI: 10.3724/SP.J.1089.2022.18946

面向效应场数值模拟的颜色映射自动调优框架

A Color Mapping Automatic Tuning Framework for Complex Simulation Datasets

  • 摘要: 在科学计算的效应场数值模拟中,变量数据的数值分布通常极不均匀,且存在大量背景噪声.针对传统数据到颜色的可视化线性映射难以获得清晰的物理特征的问题,提出一个基于颜色控制点自动调优的颜色映射框架.首先基于信息熵和高斯混合分布自动去除背景数据,然后基于累积分布函数自动调整颜色控制点位置,再基于分段亮度参数增强颜色表感知辨识性,最终生成自适应数据分布特征的颜色映射.采用爆轰冲击波、电磁辐射和高功率微波3类典型效应场模拟数据的实验结果表明,相比线性映射方法,所提框架能够获得具有高质量细节特征的可视结果,颜色辨识性可提升一个数量级,该框架是有效的.

     

    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.

     

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