ViSizer: A visualization resizing framework

Yingcai Wu, Xiaotong Liu, Shixia Liu, Kwan-Liu Ma

Research output: Contribution to journalArticlepeer-review

28 Scopus citations


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.

Original languageEnglish (US)
Article number6189339
Pages (from-to)278-290
Number of pages13
JournalIEEE Transactions on Visualization and Computer Graphics
Issue number2
StatePublished - Jan 1 2013


  • focus+context
  • nonlinear least squares optimization
  • perception
  • Resizing
  • visualization framework

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Computer Graphics and Computer-Aided Design


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