Data-Driven Colormap Optimization for 2D Scalar Field Visualization
dc.contributor.author | Zeng, Qiong | |
dc.contributor.author | Wang, Yinqiao | |
dc.contributor.author | Zhang, Jian | |
dc.contributor.author | Zhang, Wenting | |
dc.contributor.author | Tu, Changhe | |
dc.contributor.author | Viola, Ivan | |
dc.contributor.author | Wang, Yunhai | |
dc.date.accessioned | 2020-03-04T10:23:28Z | |
dc.date.available | 2020-03-04T10:23:28Z | |
dc.date.issued | 2019-12-20 | |
dc.identifier.citation | Zeng, Q., Wang, Y., Zhang, J., Zhang, W., Tu, C., Viola, I., & Wang, Y. (2019). Data-Driven Colormap Optimization for 2D Scalar Field Visualization. 2019 IEEE Visualization Conference (VIS). doi:10.1109/visual.2019.8933764 | |
dc.identifier.doi | 10.1109/VISUAL.2019.8933764 | |
dc.identifier.uri | http://hdl.handle.net/10754/661873 | |
dc.description.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. | |
dc.description.sponsorship | 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 | |
dc.publisher | Institute of Electrical and Electronics Engineers (IEEE) | |
dc.relation.url | https://ieeexplore.ieee.org/document/8933764/ | |
dc.rights | Archived with thanks to IEEE | |
dc.title | Data-Driven Colormap Optimization for 2D Scalar Field Visualization | |
dc.type | Conference Paper | |
dc.contributor.department | Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division | |
dc.contributor.department | Computer Science Program | |
dc.contributor.department | Visual Computing Center (VCC) | |
dc.conference.date | 2019-10-20 to 2019-10-25 | |
dc.conference.name | 2019 IEEE Visualization Conference, VIS 2019 | |
dc.conference.location | Vancouver, BC, CAN | |
dc.eprint.version | Pre-print | |
dc.contributor.institution | Shandong University | |
dc.contributor.institution | Chinese Academy of Sciences,Computer Network Information Center | |
kaust.person | Viola, Ivan | |
kaust.grant.number | BAS/1/1680-01-01 | |
kaust.acknowledged.supportUnit | Core Labs |
This item appears in the following Collection(s)
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Conference Papers
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Computer Science Program
For more information visit: https://cemse.kaust.edu.sa/cs -
Visual Computing Center (VCC)
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Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division
For more information visit: https://cemse.kaust.edu.sa/