Optimality of Greedy Algorithm for Generating Just-Noticeable Difference Surfaces

Tuning multimedia applications at run time to achieve high perceptual quality entails the search of nonlinear mappings that determine how control inputs should be set in order to lead to high user-perceived quality. Offline subjective tests are often used for this purpose but they are expensive to conduct because each can only evaluate one mapping at a time and there can be infinitely many such mappings to be evaluated. In this paper, we present a greedy algorithm that uses a small number of subjective test results to accurately approximate this space of mappings. Based on an axiom on monotonicity and the property of just-noticeable differences, we prove its optimality in minimizing the average absolute error between the approximate and the original mappings. We further demonstrate the results using numerical simulations and the application of the mappings found to tune the control of the multimedia game BZFlag.