Existing image-difference measures show excellent accuracy in predicting distortions, such as lossy compression, noise, and blur. Their performance on certain other distortions could be improved; one example of this is gamut mapping. This is partly because they either do not interpret chromatic information correctly or they ignore it entirely. We present an image-difference framework that comprises image normalization, feature extraction, and feature combination. Based on this framework, we create image-difference measures by selecting specific implementations for each of the steps. Particular emphasis is placed on using color information to improve the assessment of gamut-mapped images. Our best image-difference measure shows significantly higher prediction accuracy on a gamut-mapping dataset than all other evaluated measures.

We show that conjoint analysis, a popular multi-attribute preference assessment technique used in market research, is a well suited tool to evaluate a multitude of gamut mapping algorithms simultaneously. Our analysis is based on data from psycho-visual tests assessed in a laboratory and in a web environment. Conjoint analysis allows us to quantify the contribution of every single parameter value to the perceived value of the algorithm; it also allows us to test the influence of additional parameters like gamut size or color shifts. We show that conjoint analysis can be individualized to images or observers if enough data is available. Especially promising in this respect is the combination of individual and population data.

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.

We explore the potential of image-dependent gamut mapping as a constrained optimization problem. The performance of our new approach is compared to standard reference gamut mapping algorithms in psycho-visual tests.

Our topic is the potential of combining global gamut mapping with spatial methods to retain the percepted local image information in gamut mapping algorithms. The main goal is to recover the original local contrast between neighboring pixels in addition to the usual optimization of preserving lightness, saturation, and global contrast. Special emphasis is placed on avoiding artifacts introduced by the gamut mapping algorithm itself. We present an unsharp masking technique based on an edge-preserving smoothing algorithm allowing to avoid halo artifacts. The good performance of the presented approach is verified by a psycho-visual experiment using newspaper printing as a representative of a small destination gamut application. Furthermore, the improved mapping properties are documented with local mapping histograms.

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.