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Related Experiment Video

Updated: Jan 10, 2026

Measuring the Structure, Composition, and Change of Underwater Environments with Large-area Imaging

Measuring the Structure, Composition, and Change of Underwater Environments with Large-area Imaging

Published on: April 18, 2025

Multiscale structural complexity as a quantitative measure of visual complexity.

Anna Kravchenko¹, Andrey A Bagrov¹, Mikhail I Katsnelson¹

¹Radboud University, Nijmegen, the Netherlands.

|November 20, 2025

Summary

This summary is machine-generated.

We introduce multiscale structural complexity (MSSC), a new computational measure for visual complexity. MSSC effectively quantifies image complexity, correlating with human perception and offering an intuitive, efficient approach.

Keywords:

complexity modeling vision visual complexity

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Area of Science:

Computer Vision
Cognitive Science
Image Analysis

Background:

Defining and quantifying visual complexity is challenging.
Human perception of complexity is subjective and difficult to model computationally.

Purpose of the Study:

To introduce and validate the multiscale structural complexity (MSSC) measure for quantifying visual complexity.
To assess the correlation between MSSC and human subjective complexity ratings.

Main Methods:

Applying the MSSC measure to an open dataset of images (SAVOIAS) with human-rated complexity scores.
Comparing MSSC performance against other computational complexity measures.

Main Results:

MSSC demonstrates a strong correlation with subjective complexity ratings.
MSSC is more intuitive, consistent across image categories, and computationally efficient than existing measures.
The multiscale nature of MSSC allows for deeper analysis of perceived complexity.

Conclusions:

MSSC provides a robust and intuitive method for quantifying visual complexity.
This measure aligns well with human perception and offers advantages in computation and consistency.
MSSC facilitates further research into the nuances of human visual complexity perception.