Efficient quantization of painting images by relevant colors

Zeinab Tirandaz, David Foster, Javier Romero, Juan L. Nieves

Research output: Contribution to journalArticlepeer-review


Realistic images often contain complex variations in color, which can make economical descriptions difficult. Yet human observers can readily reduce the number of colors in paintings to a small proportion they judge as relevant. These relevant colors provide a way to simplify images by effectively quantizing them. The aim here was to estimate the information captured by this process and to compare it with algorithmic estimates of the maximum information possible by colorimetric and general optimization methods. The images tested were of 20 conventionally representational paintings. Information was quantified by Shannon’s mutual information. It was found that the estimated mutual information in observers’ choices reached about 90% of the algorithmic maxima. For comparison, JPEG compression delivered somewhat less. Observers seem to be efficient at effectively quantizing colored images, an ability that may have applications in the real world.
Original languageEnglish
Article number3034
Number of pages10
JournalScientific Reports
Early online date21 Feb 2023
Publication statusPublished - 21 Feb 2023


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