Professional designers and artists are quite cognizant of the rules that guide the design of effective color palettes, from both aesthetic and attention-guiding points of view. In the field of visualization, however, the use of systematic rules embracing these aspects has received less attention. The situation is further complicated by the fact that visualization often uses semi-transparencies to reveal occluded objects, in which case the resulting color mixing effects add additional constraints to the choice of the color palette. Color design forms a crucial part in visual aesthetics. Thus, the consideration of these issues can be of great value in the emerging field of illustrative visualization. We describe a knowledge-based system that captures established color design rules into a comprehensive interactive framework, aimed to aid users in the selection of colors for scene objects and incorporating individual preferences, importance functions, and overall scene composition. Our framework also offers new knowledge and solutions for the mixing, ordering and choice of colors in the rendering of semi-transparent layers and surfaces. All design rules are evaluated via user studies, for which we extend the method of conjoint analysis to task-based testing scenarios. Our framework's use of principles rooted in color design with application for the illustration of features in pre-classified data distinguishes it from existing systems which target the exploration of continuous-range density data via perceptual color maps.

Visualization algorithms can have a large number of parameters, making the space of possible rendering results rather high-dimensional. Only a systematic analysis of the perceived quality can truly reveal the optimal setting for each such parameter. However, an exhaustive search in which all possible parameter permutations are presented to each user within a study group would be infeasible to conduct. Additional complications may result from possible parameter co-dependencies. Here, we will introduce an efficient user study design and analysis strategy that is geared to cope with this problem. The user feedback is fast and easy to obtain and does not require exhaustive parameter testing. To enable such a framework we have modified a preference measuring methodology, conjoint analysis, that originated in psychology and is now also widely used in market research. We demonstrate our framework by a study that measures the perceived quality in volume rendering within the context of large parameter spaces.

The size and resolution of volume datasets in science and medicine are increasing at a rate much greater than the resolution of the screens used to view them. This limits the amount of data that can be viewed simultaneously, potentially leading to a loss of overall context of the data when the user views or zooms into a particular area of interest. We propose a focus+context framework that uses various standard and advanced magnification lens rendering techniques to magnify the features of interest, while compressing the remaining volume regions without clipping them away completely. Some of these lenses can be interactively configured by the user to specify the desired magnification patterns, while others are feature-adaptive. All our lenses are accelerated on the GPU. They allow the user to interactively manage the available screen area, dedicating more area to the more resolution-important features.

A common deficiency of discretized datasets is that detail beyond the resolution of the dataset has been irrecoverably lost. This lack of detail becomes immediately apparent once one attempts to zoom into the dataset and only recovers blur. We describe a method that generates the missing detail from any available and plausible high-resolution data, using texture synthesis. Since the detail generation process is guided by the underlying image or volume data and is designed to fill in plausible detail in accordance with the coarse structure and properties of the zoomed-in neighborhood, we refer to our method as constrained texture synthesis. Regular zooms become "semantic zooms", where each level of detail stems from a data source attuned to that resolution. We demonstrate our approach by a medical application - the visualization of a human liver - but its principles readily apply to any scenario, as long as data at all resolutions are available. We first present a 2D viewing application, called the "virtual microscope", and then extend our technique to 3D volumetric viewing.

The level-of-detail modeling method is an effective approach for the interactive visualization of complex models. In this paper, we propose a hierarchical tree structure based on the fractal dimension to organize a 3D terrain mesh model and to realize view-dependent continuous level-of-detail rendering of terrain data with a restricted quadtree triangulation method. The total algorithm can be divided into two stages: pre-processing and rendering. In pre-processing, it builds an adaptive hierarchical structure by considering the flatness of the terrain surface evaluated by the fractal dimension. Then it can generate a triangular approximation mesh by dynamically determining preserved data points according to the view-related parameters and performing a restricted quadtree triangulation. Some experiments demonstrate that it is simple and efficient and can be used to support the interactive dynamic rendering of terrain models.