Gaussian Transfer Functions for Multi-Field Volume Visualization
Proceedings of the 14th IEEE Visualization 2003 (VIS'03)
Temporal Visualization of Planning Polygons for Efficient Partitioning of Geo-Spatial Data
INFOVIS '05 Proceedings of the Proceedings of the 2005 IEEE Symposium on Information Visualization
An Approach to the Perceptual Optimization of Complex Visualizations
IEEE Transactions on Visualization and Computer Graphics
Illustrative Context-Preserving Exploration of Volume Data
IEEE Transactions on Visualization and Computer Graphics
IEEE Transactions on Visualization and Computer Graphics
Saliency-guided Enhancement for Volume Visualization
IEEE Transactions on Visualization and Computer Graphics
Dynamic View Selection for Time-Varying Volumes
IEEE Transactions on Visualization and Computer Graphics
A perceptual framework for comparisons of direct volume rendered images
PSIVT'06 Proceedings of the First Pacific Rim conference on Advances in Image and Video Technology
A perceptive evaluation of volume rendering techniques
ACM Transactions on Applied Perception (TAP)
Conjoint analysis for evaluating parameterized gamut mapping algorithms
IEEE Transactions on Image Processing
SG'11 Proceedings of the 11th international conference on Smart graphics
Estimation and modeling of actual numerical errors in volume rendering
EuroVis'10 Proceedings of the 12th Eurographics / IEEE - VGTC conference on Visualization
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Visualization algorithms can have a large number of parameters, making the space of possible rendering results rather high-dimensional. Only a systematic analysis of the perceived quality can truly reveal the optimal setting for each such parameter. However, an exhaustive search in which all possible parameter permutations are presented to each user within a study group would be infeasible to conduct. Additional complications may result from possible parameter co-dependencies. Here, we will introduce an efficient user study design and analysis strategy that is geared to cope with this problem. The user feedback is fast and easy to obtain and does not require exhaustive parameter testing. To enable such a framework we have modified a preference measuring methodology, conjoint analysis, that originated in psychology and is now also widely used in market research. We demonstrate our framework by a study that measures the perceived quality in volume rendering within the context of large parameter spaces.