Data-Driven Colormap Adjustment for Exploring Spatial Variations in Scalar Fields
Qiong Zeng1 Yongwei Zhao1 Yinqiao Wang1 Jian Zhang2 Yi Cao3 Changhe Tu1 Ivan Viola4 Yunhai Wang1
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 histogram 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.
This research was supported by the grants of NSFC (61602273, 61772315, 61861136012, 61772318), the Special Project of Science and Technology Innovation Base of Key Laboratory of Shandong Province for Software Engineering (11480004042015), and the funding from King Abdullah University of Science and Technology (KAUST) under award number BAS/1/1680-01-01. Part of this research was conducted using resources at the Visualization Core Lab at KAUST. The authors would 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, Zhi Zeng at University of South China and Qianqian Guo at Shandong University (China) for providing precious visualization resources and evaluating the quality of our cases. The authors would like to thank Christian Tominski at University of Rostock for providing valuable discussions and source codes. The authors would also like to thank the anonymous reviewers and the associate editor for precious encouragement, suggestions and comments.