With the rapid development in graphics hardware and volume rendering techniques, many volumetric datasets can now be rendered in real time on a standard PC equipped with a commodity graphics board. However, the effectiveness of the results, especially direct volume rendered images, is difficult to validate and users may not be aware of ambiguous or even misleading information in the results. This limits the applications of volume visualization. In this paper, we introduce four quantitative effectiveness measures: distinguishability, contour clarity, edge consistency, and depth coherence measures, which target different effectiveness issues for direct volume rendered images. Based on the measures, we develop a visualization system with automatic effectiveness assessment, providing users with instant feedback on the effectiveness of the results. The case study and user evaluation have demonstrated the high potential of our system.

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.

In this paper we present a novel focus+context zooming technique, which allows users to zoom into a route and its associated landmarks in a 3D urban environment from a 45-degree bird's-eye view. Through the creative utilization of the empty space in an urban environment, our technique can informatively reveal the focus region and minimize distortions to the context buildings. We first create more empty space in the 2D map by broadening the road with an adapted seam carving algorithm. A grid-based zooming technique is then used to enlarge the landmarks to reclaim the created empty space and thus reduce distortions to the other parts. Finally, an occlusion-free route visualization scheme adaptively scales the buildings occluding the route to make the route always visible to users. Our method can be conveniently integrated into Google Earth and Virtual Earth to provide seamless route zooming and help users better explore a city and plan their tours. It can also be used in other applications such as information overlay to a virtual city.

Volume exploration is an important issue in scientific visualization. Research on volume exploration has been focused on revealing hidden structures in volumetric data. While the information of individual structures or features is useful in practice, spatial relations between structures are also important in many applications and can provide further insights into the data. In this paper, we systematically study the extraction, representation, exploration, and visualization of spatial relations in volumetric data and propose a novel relation-aware visualization pipeline for volume exploration. In our pipeline, various relations in the volume are first defined and measured using region connection calculus (RCC) and then represented using a graph interface called relation graph. With RCC and the relation graph, relation query and interactive exploration can be conducted in a comprehensive and intuitive way. The visualization process is further assisted with relation-revealing viewpoint selection and color and opacity enhancement. We also introduce a quality assessment scheme which evaluates the perception of spatial relations in the rendered images. Experiments on various datasets demonstrate the practical use of our system in exploratory visualization.