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

8 Scopus citations

Abstract

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)
PublisherIEEE
Pages266-270
Number of pages5
ISBN (Print)9781728149417
DOIs
StatePublished - Dec 20 2019

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledged KAUST grant number(s): BAS/1/1680-01-01
Acknowledgements: This research was supported by the grants of NSFC (61602273, 61772315, 61861136012), Science Challenge Project (TZ2016002), and by the funding from King Abdullah University of Science and Technology (KAUST) under award number BAS/1/1680-01-01. This research used resources of the Core Labs of KAUST. The authors would also like to thank Kresimir Matkovic at VRVis Center for Virtual Reality and Visualisation GmbH (Vienna, Austria), Renata Raidou at TU Wien (Austria), Michael Böttinger at Deutsches Klimarechenzentrum (Germany), Thomas Theussl at KAUST (Saudi Arabia), Mingkui Li at Ocean University of China and Qianqian Guo at Shandong University (China) for providing precious visualization resources and evaluating the quality of our cases

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