# Minimum Description Length Clustering to Measure Meaningful Image Complexity

arXiv: https://arxiv.org/abs/2306.14937 · HTML: https://mlsanity.com/paper/2306.14937/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 72 references that are in the map.
- First posted on arXiv 26 Jun 2023.

## Builds on (most cited first, 7 of 7)

- [Describing Textures in the Wild](https://mlsanity.com/paper/1311.3618/) (2013)
- [A Differential Entropy Estimator for Training Neural Networks](https://mlsanity.com/paper/2202.06618/) (2022)
- [Leveraging Image Complexity in Macro-Level Neural Network Design for Medical Image Segmentation](https://mlsanity.com/paper/2112.11065/) (2021)
- [Selective Pseudo-label Clustering](https://mlsanity.com/paper/2107.10692/) (2021)
- [Fully Context-Aware Image Inpainting with a Learned Semantic Pyramid](https://mlsanity.com/paper/2112.04107/) (2021)
- [Deep Image Compression Using Scene Text Quality Assessment](https://mlsanity.com/paper/2305.11373/) (2023)
- [Quantifying the pathways to life using assembly spaces](https://mlsanity.com/paper/1907.04649/) (2019)

## Built on by (most cited first, 10 of 10)

- [A Modular Approach for Multimodal Summarization of TV Shows](https://mlsanity.com/paper/2403.03823/) (2024)
- [VisualOverload: Probing Visual Understanding of VLMs in Really Dense Scenes](https://mlsanity.com/paper/2509.25339/) (2025)
- [Parameter-free Video Segmentation for Vision and Language Understanding](https://mlsanity.com/paper/2503.01201/) (2025)
- [K*-Means: A Parameter-free Clustering Algorithm](https://mlsanity.com/paper/2505.11904/) (2025)
- [Towards a Universal Method for Meaningful Signal Detection](https://mlsanity.com/paper/2408.00016/) (2024)
- [Local Compositional Complexity: How to Detect a Human-readable Messsage](https://mlsanity.com/paper/2501.03664/) (2025)
- [Predicting Reaction Time to Comprehend Scenes with Foveated Scene Understanding Maps](https://mlsanity.com/paper/2505.12660/) (2025)
- [Simplicity in Complexity : Explaining Visual Complexity using Deep Segmentation Models](https://mlsanity.com/paper/2403.03134/) (2024)
- [Task-Aware Resolution Optimization for Visual Large Language Models](https://mlsanity.com/paper/2510.09822/) (2025)
- [Camera trap classification with deep learning under ground truth uncertainty](https://mlsanity.com/paper/2608.30789/) (2026)

## Abstract (arXiv)

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.