Section: New Results
Participants : Petra Isenberg [correspondant] , Pierre Dragicevic, Wesley Willett, Anastasia Bezerianos, Jean-Daniel Fekete.
We investigated hybrid-image visualization for data analysis in large-scale viewing environments. Hybrid-image visualizations blend two different visual representations into a single static view, such that each representation can be perceived at a different viewing distance. Our work was motivated by data analysis scenarios that incorporate one or more displays with sufficiently large size and resolution to be comfortably viewed by different people from various distances. Hybrid-image visualizations can be used, in particular, to enhance overview tasks from a distance and detail-in-context tasks when standing close to the display. By taking advantage of humans' perceptual capabilities, hybrid-image visualizations do not require tracking of viewers in front of a display. Moreover, because hybrid-images use a perception-based blending approach, visualizations intended for different distances can each utilize the entire display. In our paper we contributed a design space, discussed the perceptual rationale for our work, provided examples and a set of techniques for hybrid-image visualizations, and described tools for designing hybrid-image visualizations. We will also release software that will help in the construction of hybrid-image visualizations.