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ViSizer: A Visualization Resizing Framework
Feb. 2013 (vol. 19 no. 2)
pp. 278-290
Yingcai Wu, Dept. of Comput. Sci., Univ. of California, Davis, Davis, CA, USA
Xiaotong Liu, Dept. of Comput. Sci. & Eng., Ohio State Univ., Columbus, OH, USA
Shixia Liu, Microsoft Res. Asia, Beijing, China
Kwan-Liu Ma, Dept. of Comput. Sci., Univ. of California, Davis, Davis, CA, USA
Visualization resizing is useful for many applications where users may use different display devices. General resizing techniques (e.g., uniform scaling) and image-resizing techniques suffer from several drawbacks, as they do not consider the content of the visualizations. This work introduces ViSizer, a perception-based framework for automatically resizing a visualization to fit any display. We formulate an energy function based on a perception model (feature congestion), which aims to determine the optimal deformation for every local region. We subsequently transform the problem into an optimization problem by the energy function. An efficient algorithm is introduced to iteratively solve the problem, allowing for automatic visualization resizing.
Index Terms:
optimisation,data visualisation,display devices,image processing,least squares approximations,nonlinear least squares optimization,ViSizer,visualization resizing framework,display device,general resizing technique,uniform scaling,image-resizing technique,perception-based framework,energy function,perception model,feature congestion,optimal deformation,optimization problem,Visualization,Clutter,Data visualization,Optimization,Context,Layout,Ellipsoids,nonlinear least squares optimization,Resizing,visualization framework,perception,focus+context
Yingcai Wu, Xiaotong Liu, Shixia Liu, Kwan-Liu Ma, "ViSizer: A Visualization Resizing Framework," IEEE Transactions on Visualization and Computer Graphics, vol. 19, no. 2, pp. 278-290, Feb. 2013, doi:10.1109/TVCG.2012.114
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