Data-Driven Colormap Optimization for 2D Scalar Field Visualization

Qiong Zeng, Yinqiao Wang, Jian Zhang, Wenting Zhang, Changhe Tu, Ivan Viola, Yunhai Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Scopus citations


Colormapping is an effective and popular visual representation to analyze data patterns for 2D scalar fields. Scientists usually adopt a default colormap and adjust it to fit data in a trial-and-error process. Even though a few colormap design rules and measures are proposed, there is no automatic algorithm to directly optimize a default colormap for better revealing spatial patterns hidden in unevenly distributed data, especially the boundary characteristics. To fill this gap, we conduct a pilot study with six domain experts and summarize three requirements for automated colormap adjustment. We formulate the colormap adjustment as a nonlinear constrained optimization problem, and develop an efficient GPU-based implementation accompanying with a few interactions. We demonstrate the usefulness of our method with two case studies.
Original languageEnglish (US)
Title of host publication2019 IEEE Visualization Conference (VIS)
Number of pages5
ISBN (Print)9781728149417
StatePublished - Dec 20 2019


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