Minimum Description Length Clustering to Measure Meaningful Image Complexity

TL;DR

This paper introduces a hierarchical clustering-based image complexity metric that accurately distinguishes meaningful content from noise by using the minimum description length principle, capturing complexity at multiple scales.

Contribution

The novel method applies hierarchical clustering with MDL to measure meaningful image complexity, effectively differentiating noise from significant content and analyzing multi-scale complexity.

Findings

Accurately scores images, distinguishing noise from meaningful content.

Reveals complexity at different scales through hierarchical analysis.

Maintains robustness under noise addition and resolution changes.

Abstract

Existing image complexity metrics cannot distinguish meaningful content from noise. This means that white noise images, which contain no meaningful information, are judged as highly complex. We present a new image complexity metric through hierarchical clustering of patches. We use the minimum description length principle to determine the number of clusters and designate certain points as outliers and, hence, correctly assign white noise a low score. The presented method has similarities to theoretical ideas for measuring meaningful complexity. We conduct experiments on seven different sets of images, which show that our method assigns the most accurate scores to all images considered. Additionally, comparing the different levels of the hierarchy of clusters can reveal how complexity manifests at different scales, from local detail to global structure. We then present ablation studies showing the contribution of the components of our method, and that it continues to assign reasonable scores when the inputs are modified in certain ways, including the addition of Gaussian noise and the lowering of the resolution.