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    Data-driven Colormap Adjustment for Exploring Spatial Variations in Scalar Fields

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    Name:
    Data_Driven_Colormap_Adaptation_for_Exploring_Spatial_Variations_in_Scalar_Fields.pdf
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    22.60Mb
    Format:
    PDF
    Description:
    Accepted manuscript
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    Type
    Article
    Authors
    Zeng, Qiong
    Zhao, Yongwei
    Wang, Yinqiao
    Zhang, Jian
    Cao, Yi
    Tu, Changhe
    Viola, Ivan cc
    Wang, Yunhai
    KAUST Department
    Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division
    Computer Science Program
    Visual Computing Center (VCC)
    Date
    2021-09-01
    Online Publication Date
    2021
    Print Publication Date
    2022-12-01
    Permanent link to this record
    http://hdl.handle.net/10754/670899
    
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    Abstract
    Colormapping is an effective and popular visualization technique for analyzing patterns in scalar fields. Scientists usually adjust a default colormap to show hidden patterns by shifting the colors in a trial-and-error process. To improve efficiency, efforts have been made to automate the colormap adjustment process based on data properties (e.g., statistical data value or distribution). However, as the data properties have no direct correlation to the spatial variations, previous methods may be insufficient to reveal the dynamic range of spatial variations hidden in the data. To address the above issues, we conduct a pilot analysis with domain experts and summarize three requirements for the colormap adjustment process. Based on the requirements, we formulate colormap adjustment as an objective function, composed of a boundary term and a fidelity term, which is flexible enough to support interactive functionalities. We compare our approach with alternative methods under a quantitative measure and a qualitative user study (25 participants), based on a set of data with broad distribution diversity. We further evaluate our approach via three case studies with six domain experts. Our method is not necessarily more optimal than alternative methods of revealing patterns, but rather is an additional color adjustment option for exploring data with a dynamic range of spatial variations.
    Citation
    Zeng, Q., Zhao, Y., Wang, Y., Zhang, J., Cao, Y., Tu, C., … Wang, Y. (2021). Data-driven Colormap Adjustment for Exploring Spatial Variations in Scalar Fields. IEEE Transactions on Visualization and Computer Graphics, 1–1. doi:10.1109/tvcg.2021.3109014
    Publisher
    IEEE
    Journal
    IEEE Transactions on Visualization and Computer Graphics
    DOI
    10.1109/TVCG.2021.3109014
    Additional Links
    https://ieeexplore.ieee.org/document/9527154/
    https://ieeexplore.ieee.org/document/9527154/
    https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9527154
    ae974a485f413a2113503eed53cd6c53
    10.1109/TVCG.2021.3109014
    Scopus Count
    Collections
    Articles; Computer Science Program; Visual Computing Center (VCC); Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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