Title:	DATA-DRIVEN APPROACH FOR GENERATING COLORMAPS OF SCIENTIFIC SIMULATION DATA
Author(s):	Yi Cao, Xiaohua Wang, Huawei Wang, Zhiwei Ai, and Fang Xia
ISBN:	978-989-8704-21-4
Editors:	Yingcai Xiao, Ajith P. Abraham and Jörg Roth
Year:	2020
Edition:	Single
Keywords:	Colormaps, Color Perception, Information Theory, Scientific Visualization
Type:	Full
First Page:	3
Last Page:	10
Language:	English
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Paper Abstract:	The colormap plays important roles in the exploration, analysis, and discovery of scientific data. Datasets generated by real-world simulation applications are becoming increasingly more complex: the distribution of the variable values within the data is often extremely uneven and accompanied by high levels of data noise. The heterogeneity of these data characteristics presents rich information about physical laws as well as technical challenges for colormap of visualization. Most default colormaps are defined by linearly interpolating dataset values and cannot be adapted to convey the physical meaning behind complicated data. In this paper, we introduce a data-driven approach for generating colormaps of scientific simulation data, that can overcome the visualization problems using noise-reduction-based parameter tuning of color control points and a wave-like colormap enhancement in brightness. Several real-world simulation data are used to verify the effectiveness of our proposed method, which means that our method could help domain scientists understand complicated data more clearly and quickly.
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