Perceptually Based Depth-Ordering Enhancement
for Direct Volume Rendering

Lin Zheng¹     Yingcai Wu²     Kwan-Liu Ma¹
This project was conducted when Yingcai Wu worked in UC Davis.
¹University of California, Davis      ²Microsoft Research Asia

Experiment on an Neghip volume. (a) An original image in which the depth relations between structures are misleading. (b) An image enhanced by our optimization approach based on merely the depth ordering energy. (c) An image enhanced by our optimization approach based on both the depth ordering and transparency energy. (d) An image enhanced by our optimization approach based on the depth ordering, transparency, and image faithfulness energy. (e) Illustration of two X-Junctions in (a). The X-junction in the orange rectangle shows a weak C configuration. The other in the green rectangle reveals an Z configuration. (f) Illustration of two selected X-Junctions in (d).

Visualizing complex volume data usually renders selected parts of the volume semi-transparently to see inner structures of the volume or provide a context. This presents a challenge for volume rendering methods to produce images with unambiguous depthordering perception. Existing methods use visual cues such as halos and shadows to enhance depth perception. Along with other limitations, these methods introduce redundant information and require additional overhead. This paper presents a new approach to enhancing depth-ordering perception of volume rendered images without using additional visual cues. We set up an energy function based on quantitative perception models to measure the quality of the images in terms of the effectiveness of depth-ordering and transparency perception as well as the faithfulness of the information revealed. Guided by the function, we use a conjugate gradient method to iteratively and judiciously enhance the results. Our method can complement existing systems for enhancing volume rendering results. The experimental results demonstrate the usefulness and effectiveness of our approach.

@article {YWu2013b,
author = {Lin Zheng and Yingcai Wu and Kwan-Liu Ma},
title = {Perceptually Based Depth-Ordering Enhancement for Direct Volume Rendering,
journal = {IEEE Transactions on Visualization and Computer Graphics,
year = {2013},
volume = {19},
number = {3},
pages = {446--459}
}

This research was supported in part by the HP Labs and U.S. National Science Foundation through grants CCF-0808896, CNS-0716691, CCF 0811422, CCF 0938114, and CCF-1025269.