# Compression ensembles quantify aesthetic complexity and the evolution of visual art

arXiv: https://arxiv.org/abs/2205.10271 · HTML: https://mlsanity.com/paper/2205.10271/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 107 references that are in the map.
- First posted on arXiv 20 May 2022.

## Builds on (most cited first, 15 of 15)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Neural Machine Translation of Rare Words with Subword Units](https://mlsanity.com/paper/1508.07909/) (2015)
- [Automated Concatenation of Embeddings for Structured Prediction](https://mlsanity.com/paper/2010.05006/) (2020)
- [The emotional arcs of stories are dominated by six basic shapes](https://mlsanity.com/paper/1606.07772/) (2016)
- [The Shape of Art History in the Eyes of the Machine](https://mlsanity.com/paper/1801.07729/) (2018)
- [Take and Took, Gaggle and Goose, Book and Read: Evaluating the Utility of Vector Differences for Lexical Relation Learning](https://mlsanity.com/paper/1509.01692/) (2015)
- [Mapping the NFT revolution: market trends, trade networks and visual features](https://mlsanity.com/paper/2106.00647/) (2021)
- [OmniArt: Multi-task Deep Learning for Artistic Data Analysis](https://mlsanity.com/paper/1708.00684/) (2017)
- [Conceptual similarity and communicative need shape colexification: an experimental study](https://mlsanity.com/paper/2103.11024/) (2021)
- [Quantifying Creativity in Art Networks](https://mlsanity.com/paper/1506.00711/) (2015)
- [Graph Neural Networks for Knowledge Enhanced Visual Representation of Paintings](https://mlsanity.com/paper/2105.08190/) (2021)
- [The Distributed Information Bottleneck reveals the explanatory structure of complex systems](https://mlsanity.com/paper/2204.07576/) (2022)
- [Complexity and Aesthetics in Generative and Evolutionary Art](https://mlsanity.com/paper/2201.01470/) (2022)
- [A simple method for estimating the fractal dimension from digital images: The compression dimension](https://mlsanity.com/paper/1602.02139/) (2016)

## Built on by (most cited first, 9 of 9)

- [Machine-assisted quantitizing designs: augmenting humanities and social sciences with artificial intelligence](https://mlsanity.com/paper/2309.14379/) (2023)
- [Expertise elevates AI usage: experimental evidence comparing laypeople and professional artists](https://mlsanity.com/paper/2501.12374/) (2025)
- [Collection Space Navigator: An Interactive Visualization Interface for Multidimensional Datasets](https://mlsanity.com/paper/2305.06809/) (2023)
- [Context-aware Multimodal AI Reveals Hidden Pathways in Five Centuries of Art Evolution](https://mlsanity.com/paper/2503.13531/) (2025)
- [fruit-SALAD: A Style Aligned Artwork Dataset to reveal similarity perception in image embeddings](https://mlsanity.com/paper/2406.01278/) (2024)
- [Investigating the diversity and stylization of contemporary user generated visual arts in the complexity entropy plane](https://mlsanity.com/paper/2408.10356/) (2024)
- [Surprise machines: revealing Harvard Art Museums' image collection](https://mlsanity.com/paper/2308.09343/) (2023)
- [Simplicity in Complexity : Explaining Visual Complexity using Deep Segmentation Models](https://mlsanity.com/paper/2403.03134/) (2024)
- [Studying Comics at Scale with Artificial Intelligence: The PAGE Framework and Three Studies of American Comics and Japanese Manga](https://mlsanity.com/paper/2610.13840/) (2026)

## Abstract (arXiv)

The quantification of visual aesthetics and complexity have a long history, the latter previously operationalized via the application of compression algorithms. Here we generalize and extend the compression approach beyond simple complexity measures to quantify algorithmic distance in historical and contemporary visual media. The proposed "ensemble" approach works by compressing a large number of transformed versions of a given input image, resulting in a vector of associated compression ratios. This approach is more efficient than other compression-based algorithmic distances, and is particularly suited for the quantitative analysis of visual artifacts, because human creative processes can be understood as algorithms in the broadest sense. Unlike comparable image embedding methods using machine learning, our approach is fully explainable through the transformations. We demonstrate that the method is cognitively plausible and fit for purpose by evaluating it against human complexity judgments, and on automated detection tasks of authorship and style. We show how the approach can be used to reveal and quantify trends in art historical data, both on the scale of centuries and in rapidly evolving contemporary NFT art markets. We further quantify temporal resemblance to disambiguate artists outside the documented mainstream from those who are deeply embedded in Zeitgeist. Finally, we note that compression ensembles constitute a quantitative representation of the concept of visual family resemblance, as distinct sets of dimensions correspond to shared visual characteristics otherwise hard to pin down. Our approach provides a new perspective for the study of visual art, algorithmic image analysis, and quantitative aesthetics more generally.