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Perceptually Based Depth-Ordering Enhancement for Direct Volume Rendering

By Lin Zheng, Yingcai Wu and Kwan-liu Ma

Abstract

Abstract—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. Index Terms—volume rendering, depth ordering, depth perception, transparency, visualization

Year: 2014
OAI identifier: oai:CiteSeerX.psu:10.1.1.412.1384
Provided by: CiteSeerX
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