Perception-Based Transparency Optimization for Direct Volume Rendering

Abstract

The semi-transparent nature of direct volume rendered images is useful to depict layered structures in a volume. However, obtaining a semi-transparent result with the layers clearly revealed is difficult and may involve tedious adjustment on opacity and other rendering parameters. Furthermore, the visual quality of layers also depends on various perceptual factors. In this paper, we propose an auto-correction method for enhancing the perceived quality of the semi-transparent layers in direct volume rendered images. We introduce a suite of new measures based on psychological principles to evaluate the perceptual quality of transparent structures in the rendered images. By optimizing rendering parameters within an adaptive and intuitive user interaction process, the quality of the images is enhanced such that specific user requirements can be met. Experimental results on various datasets demonstrate the effectiveness and robustness of our method.

Human Computation in Visualization: Using Purpose Driven Games for Robust Evaluation of Visualization Algorithms

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“…Success of such methods thus depends greatly on how well they are able to convey the message to the viewer. Over the years we have seen many different techniques and methods that try to conform to the knowledge we have about human perception to derive better visualization algorithms [32,35]. Due to the limitation of silicon-based computers and also the fact that we are still far away from actually building a complete analytical model of the human brain's working process, truly optimizing visualization to capture its full potential seems hard to achieve.…”

Application of Multiple Iso-Surface Rendering to Improvement of Perceived Depth in Transparent Stereoscopic Visualization

“…In this paper, we focused on providing an artificial hint to improve the accuracy of perceived depth in the iso-surface visualization of human volume data. In addition, there are many existing studies on the depth in volume rendering [5,6,7,8,9,10]. The new contribution of this research is to improve the depth perception that occurs when volume rendering is combined with stereoscopic visualization.…”

And…Action! Setting the Scene for Accurate Visual CFD Comparisons Using Ray Tracing

“…However, an often unrecognised source of comparison error stems from the way in which the figures are made [3]. While there has been some attention for these topics, such as the well-known papers on the problems with using rainbow colour maps [4][5][6], such investigations often focus on improving the figure itself, by investigating the use of different colours and how humans perceive colours [7][8][9], or how to to best visualise vector fields [10,11]. The errors occurring in the visualisation of different data-sets, i.e., experimental-numerical, experimental-experimental or numerical-numerical, are often overlooked.…”

“…This is mainly of interest for visualisations far below the water surface, and is also highly depending on the colours of the objects of interest (see, e.g., in [30]). Error sources in computer visualisation and their effects have been extensively researched in literature (see, e.g., in [7][8][9]12,19,20,[31][32][33][34]. In this section, we will briefly comment on some of the error sources for clarification.…”