Perceptually-based depth-ordering enhancement for direct volume rendering

Lin Zheng, Yingcai Wu, Kwan-Liu Ma

Research output: Contribution to journalArticle

22 Scopus citations

Abstract

Visualizing complex volume data usually renders selected parts of the volume semitransparently to see inner structures of the volume or provide a context. This presents a challenge for volume rendering methods to produce images with unambiguous depth-ordering 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.

Original languageEnglish (US)
Article number6226392
Pages (from-to)446-459
Number of pages14
JournalIEEE Transactions on Visualization and Computer Graphics
Volume19
Issue number3
DOIs
StatePublished - Jan 21 2013

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Keywords

  • depth ordering
  • depth perception
  • transparency
  • visualization
  • Volume rendering

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Computer Graphics and Computer-Aided Design

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