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.

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.

We show that any comparison based, randomized algorithm to approximate any given ranking of n items within expected Spearman’s footrule distance n 2/ν(n) needs at least n (min{log ν(n), log n} – 6) comparisons in the worst case. This bound is tight up to a constant factor since there exists a deterministic algorithm that shows that 6n(log ν(n)+1) comparisons are always sufficient.