Compression ensembles quantify aesthetic complexity and the evolution of visual art

EPJ Data Science

Regular Article

Sebastian E. Ahnert¹  Mar Canet Solಠ Tillmann Ohm²  Andres Karjus²  Maximilian Schich² 

[1] Department of Chemical Engineering and Biotechnology, University of Cambridge, hilippa Fawcett Drive, CB30AS, Cambridge, UK;The Alan Turing Institute, 96 Euston Road, NW12DB, London, UK;ERA Chair for Cultural Data Analytics, Tallinn University, Narva mnt 25, 10120, Tallinn, Estonia;

关键词: Aesthetic complexity;    Kolmogorov complexity;    Image compression;    Art history;    Family resemblance;    Artistic careers;    NFT art;    Temporal resemblance;   

DOI  :  10.1140/epjds/s13688-023-00397-3

 received in 2023-01-26, accepted in 2023-05-24,  发布年份 2023

来源: Springer

【 摘 要 】

To the human eye, different images appear more or less complex, but capturing this intuition in a single aesthetic measure is considered hard. Here, we propose a computationally simple, transparent method for modeling aesthetic complexity as a multidimensional algorithmic phenomenon, which enables the systematic analysis of large image datasets. The approach captures visual family resemblance via a multitude of image transformations and subsequent compressions, yielding explainable embeddings. It aligns well with human judgments of visual complexity, and performs well in authorship and style recognition tasks. Showcasing the functionality, we apply the method to 125,000 artworks, recovering trends and revealing new insights regarding historical art, artistic careers over centuries, and emerging aesthetics in a contemporary NFT art market. Our approach, here applied to images but applicable more broadly, provides a new perspective to quantitative aesthetics, connoisseurship, multidimensional meaning spaces, and the study of cultural complexity.

【 授权许可】

CC BY   
© The Author(s) 2023

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