ViSizer: A Perception-Based Framework for Visualization Resizing

Visualization resizing is useful for many applications such as collaborative business intelligence where users may use display devices with different sizes and aspect ratios. General resizing techniques (e.g., uniform scaling) and image resizing techniques suffer from several drawbacks, as they do not consider the content of visualizations. In this work, we introduce ViSizer, a perception-based framework for automatically resizing a visualization to fit any display. It can largely preserve salient features while resizing the visualization to an arbitrary aspect ratio with minimum visual clutter introduced. To achieve this, 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 resizing problem into a non-linear optimization problem by the energy function. An efficient optimization algorithm is introduced to iteratively solve the problem, allowing for automatic visualization resizing.