Visual Analytics and Imaging Laboratory (VAI Lab)
Computer Science Department, Stony Brook University, NY
Abstract: Color mapping and semitransparent layering play an important role in many visualization scenarios, such as information
visualization and volume rendering. The combination of color and transparency is still dominated by standard alpha-compositing
using the Porter-Duff over operator which can result in false colors with deceiving impact on the visualization. Other more advanced
methods have also been proposed, but the problem is still far from being solved. Here we present an alternative to these existing
methods speci?cally devised to avoid false colors and preserve visual depth ordering. Our approach is data driven and follows
the recently formulated knowledge-assisted visualization (KAV) paradigm. Preference data, that have been gathered in web-based
user surveys, are used to train a support-vector machine model for automatically predicting an optimized hue-preserving blending.
We have applied the resulting model to both volume rendering and a speci?c information visualization technique, illustrative parallel
coordinate plots. Comparative renderings show a signi?cant improvement over previous approaches in the sense that false colors
are completely removed and important properties such as depth ordering and blending vividness are better preserved. Due to the
generality of the de?ned data-driven blending operator, it can be easily integrated also into other visualization frameworks.
Teaser: A mutli-layer Illustrative Parallel Coordinate plot with a blue layer on top and red layer in the background.
(left) blending using conventional alpha-compositing -- the false colors in the overlap regions are readily apparent; (right) blending using our data-driven blending operator -- there are no false colors and the front-back relationships are easily identified.
Paper: L. Kühne, J. Giesen, Z. Zhang, S. Ha, K. Mueller, "Data-Driven Approach to Hue-Preserving Color-Blending,"
(to appear), IEEE Transactions on Visualization and Computer Graphics,