Multi-scale structural complexity as a quantitative measure of visual complexity

TL;DR

This paper introduces the multi-scale structural complexity (MSSC) measure to quantify visual complexity, demonstrating its correlation with human subjective scores and highlighting its intuitive, consistent, and computational advantages.

Contribution

The paper proposes MSSC as a new, intuitive, and computationally efficient measure of visual complexity, validated against human subjective scores on a large image dataset.

Findings

MSSC correlates well with human subjective complexity scores.

MSSC is more intuitive and easier to compute than existing measures.

The multi-scale approach enables deeper analysis of perceived complexity.

Abstract

While intuitive for humans, the concept of visual complexity is hard to define and quantify formally. We suggest adopting the multi-scale structural complexity (MSSC) measure, an approach that defines structural complexity of an object as the amount of dissimilarities between distinct scales in its hierarchical organization. In this work, we apply MSSC to the case of visual stimuli, using an open dataset of images with subjective complexity scores obtained from human participants (SAVOIAS). We demonstrate that MSSC correlates with subjective complexity on par with other computational complexity measures, while being more intuitive by definition, consistent across categories of images, and easier to compute. We discuss objective and subjective elements inherently present in human perception of complexity and the domains where the two are more likely to diverge. We show how the multi-scale nature of MSSC allows further investigation of complexity as it is perceived by humans.