Kolmogorov compression complexity may differentiate different schools of Orthodox iconography

Daniel Peptenatu, Ion Andronache, Helmut Ahammer, Richard Taylor, Ioannis Liritzis, Marko Radulovic, Bogdan Ciobanu, Marin Burcea, Matjaz Perc, Tuan D. Pham, Bojan M. Tomić, Cosmin Iulian Cîrstea, Adrian Nicolae Lemeni, Andreea Karina Gruia, Alexandra Grecu, Marian Marin, Herbert Franz Jelinek

Research output: Contribution to journalArticlepeer-review

7 Scopus citations


The complexity in the styles of 1200 Byzantine icons painted between 13th and 16th from Greece, Russia and Romania was investigated through the Kolmogorov algorithmic information theory. The aim was to identify specific quantitative patterns which define the key characteristics of the three different painting schools. Our novel approach using the artificial surface images generated with Inverse FFT and the Midpoint Displacement (MD) algorithms, was validated by comparison of results with eight fractal and non-fractal indices. From the analyzes performed, normalized Kolmogorov compression complexity (KC) proved to be the best solution because it had the best complexity pattern differentiations, is not sensitive to the image size and the least affected by noise. We conclude that normalized KC methodology does offer capability to differentiate the icons within a School and amongst the three Schools.

Original languageBritish English
Article number10743
JournalScientific Reports
Issue number1
StatePublished - Dec 2022


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