An Entropy-Based Approach for Identifying User-Preferred Camera Positions

Viewpoint Quality (VQ) metrics have the potential to predict human preferences for camera placement. With this study, we introduce new VQ metrics that incorporate entropy, and explore how they can be used in combination. Our evaluation involves three phases: (1) creating a database of isosurface imagery from ten large, scientific data sets, (2) conducting a user study with approximately 30 large data visualization experts who provided over 1000 responses, and (3) analyzing how our entropy-based VQ metrics compared with existing VQ metrics in predicting expert preference. In terms of findings, we find that our entropy-based metrics are able to predict expert preferences 68% of the time, while existing VQ metrics perform much worse (52%). This finding, while valuable on its own, also opens the door for future work on in situ camera placement. Finally, as another important contribution, this work has the most extensive evaluation to date of existing VQ metrics to predict expert preference for visualizations of large, scientific data sets.