AbstractProfessional 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 Recent 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 transfer functions (also called color scales or color palettes) intended for the mapping of scientific or cartographic scalar data to RGB attributes. Well known for the former is the PRAVDA system [2], and for the latter the Color Brewer [4], which has been recently generalized in [35] . We address a different data scenario one in which a volume or image has been divided into a set of coherent regions, e.g., specific organs in a volumetric medical dataset, cells in a microscopic image, or graphical objects in an information display. Further, any of these regions may bear interesting intensity variations which need to be visually represented in a faithful manner. Professional designers and artists are quite cognizant of rules that guide the design of color palettes, not only from an aesthetic point of view but also from an attention-guiding, salient one. Likewise, visualization is not only concerned with providing a pleasing image it also has a mission, that is, to help users gain quick and accurate insight into the visualized data [34] . Our framework captures these known color design rules into a knowledge-based system, which then combines them with scene analysis, user preferences, and importance functions to derive appropriate colorizations. These latter considerations are typically not embodied in currently existing frameworks. The scope of our system is image-centric as well as volume visualization. Both often use semi-transparencies to reveal occluded objects. Here, color mixing effects and the perceived depth-ordering of the semi-transparent layers pose additional constraints on the color choices. Since illustrative rendering is not always physically correct, proper user studies are crucial for the parameterization of the models underlying our system. We are particularly interested in the effectiveness of a given colorization to solve certain perceptual tasks and to derive knowledge that is then applicable for practical visualization tasks. For this, we extended the method of conjoint analysis [11] from choice-based testing to task-based testing. We note that in this paper we only consider the effects of color, and not those of illustrative style and the combination of these. We believe that a decoupling of these visualization parameters is necessary to develop a rudimentary framework, which can then later be applied in the context of stylistically more advanced systems. Our paper is structured as follows. Section 2 presents related work, Section 3 gives a system overview, and Section 4 provides theoretical considerations. Section 5 focuses on the color design aspects, and Section 6 discusses the color mixing. Section 7 extends conjoint analysis to task-based testing, applies it to derive models for color mixing in an illustrative rendering context, and demonstrates their use in a specific application. Section 8 ends with conclusions. Good visual aesthetics make a visualization task an enjoyable one and therefore reduces stress, which is a significant factor in todays fast paced data analysis scenarios. As [25] suggests, it is essential that products designed for use under stress follow good humancentered (pleasant, aesthetic) design, since stress makes people less able to cope with difficulties, less flexible, and less creative in problem solving tasks. In fact, it has been shown that objects considered
Wang et al. REF explain the problem of false colors in the context of constructing a color palette for illustrative visualization.
Color Design for Illustrative Visualization
IEEE Transactions on Visualization and Computer Graphics
IEEE Transactions on Visualization and Computer Graphics
Computer Science Medicine