| Autor: |
Zeinab Tirandaz, David H. Foster, Javier Romero, Juan Luis Nieves |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
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| Zdroj: |
Scientific Reports, Vol 13, Iss 1, Pp 1-10 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2045-2322 |
| DOI: |
10.1038/s41598-023-29380-8 |
| Popis: |
Abstract 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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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