English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

The quantification of visual aesthetics and complexity have a long history,the latter previously operationalized via the application of compressionalgorithms. Here we generalize and extend the compression approach beyondsimple complexity measures to quantify algorithmic distance in historical andcontemporary visual media. The proposed "ensemble" approach works bycompressing a large number of transformed versions of a given input image,resulting in a vector of associated compression ratios. This approach is moreefficient than other compression-based algorithmic distances, and isparticularly suited for the quantitative analysis of visual artifacts, becausehuman creative processes can be understood as algorithms in the broadest sense.Unlike comparable image embedding methods using machine learning, our approachis fully explainable through the transformations. We demonstrate that themethod is cognitively plausible and fit for purpose by evaluating it againsthuman complexity judgments, and on automated detection tasks of authorship andstyle. We show how the approach can be used to reveal and quantify trends inart historical data, both on the scale of centuries and in rapidly evolvingcontemporary NFT art markets. We further quantify temporal resemblance todisambiguate artists outside the documented mainstream from those who aredeeply embedded in Zeitgeist. Finally, we note that compression ensemblesconstitute a quantitative representation of the concept of visual familyresemblance, as distinct sets of dimensions correspond to shared visualcharacteristics otherwise hard to pin down. Our approach provides a newperspective for the study of visual art, algorithmic image analysis, andquantitative aesthetics more generally.

Compression ensembles quantify aesthetic complexity and the evolution of visual art