A System for Using Perceiver Input to Vary the Quality of Generative Multimedia Performances

Abstract

Generative Multimedia (GM) applications are an increasingly popular way to implement interactive media performances. Our contributions include creating a metric for evaluating Generative Multimedia performances, designing a model for accepting perceiver preferences, and using those preferences to adapt GM performances. The metric used is imprecision, which is the ratio of the actual computation time of a GM element to the computation time of a complete version of that GM element. By taking a perceiver's preferences into account when making adaptation decisions, applications can produce GM performances that meet soft real-time and resource constraints while allocating imprecision to the GM elements the perceiver least cares about. Compared to other approaches, perceiver-directed imprecision best allocates impreciseness while minimizing delay.