Data Visualization Optimization via Computational Modeling of Perception
Abstract
We present a method for automatically evaluating and optimizing visualizations using a computational model of human vision. The method relies on a neural network simulation of early perceptual processing in the retina and primary visual cortex. The neural activity resulting from viewing flow visualizations is simulated and evaluated to produce a metric of visualization effectiveness. Visualization optimization is achieved by applying this effectiveness metric as the utility function in a hill-climbing algorithm. We apply this method to the evaluation and optimization of 2D flow visualizations, using two visualization parameterizations: streaklet-based and pixel-based. An emergent property of the streaklet-based optimization is head-to-tail streaklet alignment. It had been previously hypothesized the effectiveness of head-to-tail alignment results from the perceptual processing of the visual system, but this theory had not been computationally modeled. A second optimization using a pixel-based parameterization resulted in a LIC-like result. The implications in terms of the selection of primitives is discussed. We argue that computational models can be used for optimizing complex visualizations. In addition, we argue that they can provide a means of computationally evaluating perceptual theories of visualization, and as a method for quality control of display methods.
Department
Center for Coastal and Ocean Mapping
Publication Date
2-2012
Volume
18, Issue 2
Journal Title
IEEE Transactions on Visualization and Computer Graphics
Pages
309-320
Publisher Place
New York, NY, USA
Publisher
IEEE
Digital Object Identifier (DOI)
10.1109/TVCG.2011.52
Document Type
Journal Article
Recommended Citation
D. Pineo and C. Ware, "Data visualization optimization via computational modeling of perception," IEEE Transactions on Visualization and Computer Graphics, vol. 18, no. 2, pp. 309–320, Feb. 2012.