# Multiscale structural complexity as a quantitative measure of visual complexity

## TL;DR

This paper introduces a new way to measure visual complexity using multiscale structural complexity, which aligns well with human perception and is easier to compute.

## Contribution

The novel contribution is the introduction of multiscale structural complexity (MSSC) as an intuitive and effective measure of visual complexity.

## Key findings

- MSSC correlates with subjective complexity scores as well as other computational measures.
- MSSC is more intuitive and consistent across different image categories.
- MSSC's multiscale nature allows deeper investigation into human-perceived complexity.

## Abstract

While intuitive for humans, the concept of visual complexity is hard to define and quantify formally. We suggest adopting the multiscale 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 multiscale nature of MSSC allows further investigation of complexity as it is perceived by humans.