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 frameworks 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.Index TermsColor design, volume rendering, transparency, user study evaluation, conjoint analysis, illustrative visualization INTRODUCTIONRecent years have seen multifarious efforts to better integrate and exploit properties of human visual perception into visualization design. Illustrative rendering techniques have been developed that render the scene at different levels of abstractions [30] and detail [32] or in different rendering styles [5], with applications ranging from information visualization [20] to full-scale volume rendering. In these approaches, the levels of abstraction are most often controlled by a task-or object-dependent importance parameter [31]. Another perception-motivated strategy is to guide viewer attention to salient features [16]. Color can play a major role in these particular efforts. However, there is no illustrative rendering system so far that incorporates rules from color design directly into the visualization engine. Yet, the working scenario of graphics designers is quite similar to that of illustrative rendering. Like in graphics design, pre-classified or segmented objects typically constitute the working set. It thus seems beneficial to learn from the well-developed rules used by graphics designers, working in highly profit-oriented fields such as advertising, and adapt and modify these rules for the special needs of illustrative data visualization. The study of color for data visualization tasks is not new, yet it has traditionally focused on the design of ...

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.

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