ViSizer: A Visualization Resizing Framework

IEEE Transactions on Visualization and Computer Graphics, 19(2):278-290, 2013

Yingcai Wu1     Xiaotong Liu2     Shixia Liu1     Kwan-Liu Ma3
This project was conducted when Yingcai Wu worked in UC Davis.
1Microsoft Research Asia      2The Ohio State University      3University of California, Davis

Teaser Image

Results created by resizing a graph visualization originally shown on a 27 inch display with 1920 X 1200 pixels (top-left) to a 3.5 inch display with 960 640 pixels in different orientations using uniform scaling, ViSizer with a significance-aware grid, and ViSizer with an adaptive grid. ViSizer makes more efficient use of the small displays than uniform scaling. The adaptive and the significance-aware grids both work well in most cases for maintaining original information. However, the significance-aware grid might have a chance to produce artifacts. The node indicated by the green arrow is not well preserved in the results of the significance-aware grid.


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. Thiswork 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.

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Download the paper


@article {YWu2013c,
author = {Yingcai Wu and Xiaotong Liu and Shixia Liu and Kwan-Liu Ma},
title = {ViSizer - A Visualization Resizing Framework,
journal = {IEEE Transactions on Visualization and Computer Graphics,
year = {2013},
volume = {19},
number = {2},
pages = {278--290}


This research was supported in part by the HP Labs and U.S. National Science Foundation through grants CCF-0808896, CNS-0716691, CCF 0811422, CCF 0938114, and CCF-1025269.