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The goal of this project is to develop a quantitative data analysis framework to facilitate effective visualization of large-scale scientific data sets. By considering the process of visualization as a communication channel, we can quantitatively model the information flow between the data input and the visualization output. With information theory as the theoretical foundation, we are developing a framework to evaluate and optimize the quality of visualization based on the information content of the input data, the visualization output, and the discrepancy between the two. The framework can systematically guide the visual analysis process by iteratively optimizing the visualization result so that the information gap between the two ends of the visual analysis pipeline be quickly narrowed.
The project is supported in part by National Science Foundation [NSF project page] and Department of Energy
Publications:
Publications:
- Soumya Dutta, Xiaotong Liu, Ayan Biswas, Han-Wei Shen, and Jen-Ping Chen: Pointwise Information Guided Visual Analysis of Time-varying Multi-fields, SIGGRAPH Asia Symposium on Visualization 2017
- Tzu-Hsuan Wei, Teng-Yok Lee, Han-Wei Shen: Evaluating Isosurfaces with Level-set-based Information Maps. Comput. Graph. Forum 32(3): 1-10 (2013)
- Ayan Biswas, Soumya Dutta, Han-Wei Shen, Jonathan Woodring: An Information-Aware Framework for Exploring Multivariate Data Sets. IEEE Trans. Vis. Comput. Graph. 19(12): 2683-2692 (2013)
- Chaoli Wang, Han-Wei Shen: Information Theory in Scientific Visualization. Entropy 13(1): 254-273 (2011)
- Teng-Yok Lee, Oleg Mishchenko, Han-Wei Shen, Roger Crawfis: View point evaluation and streamline filtering for flow visualization. PacificVis 2011: 83-90
- Lijie Xu, Teng-Yok Lee, Han-Wei Shen: An Information-Theoretic Framework for Flow Visualization. IEEE Trans. Vis. Comput. Graph. 16(6): 1216-1224 (2010)
- Udeepta Bordoloi, Han-Wei Shen: View Selection for Volume Rendering. IEEE Visualization 2005: 487-494
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