Quantifying Visual Abstraction Quality for Computer-Generated Illustrations

Abstract

We investigate how the perceived abstraction quality of computer-generated illustrations is related to the number of primitives (points and small lines) used to create them. Since it is difficult to find objective functions that quantify the visual quality of such illustrations, we propose an approach to derive perceptual models from a user study. By gathering comparative data in a crowdsourcing user study and employing a paired comparison model, we can reconstruct absolute quality values. Based on an exemplary study for stippling, we show that it is possible to model the perceived quality of stippled representations based on the properties of an input image. The generalizability of our approach is demonstrated by comparing models for different stippling methods. By showing that our proposed approach also works for small lines, we demonstrate its applicability toward quantifying different representational drawing elements. Our results can be related to Weber–Fechner’s law from psychophysics and indicate a logarithmic relationship between number of rendering primitives in an illustration and the perceived abstraction quality thereof.