Abstract
Observers can adjust the spectrum of illumination on paintings for optimal viewing experience. But can they adjust the colors of paintings for the best visual impression? In an experiment carried out on a calibrated color monitor images of four abstract paintings obtained from hyperspectral data were shown to observers that were unfamiliar with the paintings. The color volume of the images could be manipulated by rotating the volume around the axis through the average (a*, b*) point for each painting in CIELAB color space. The task of the observers was to adjust the angle of rotation to produce the best subjective impression from the paintings. It was found that the distribution of angles selected for data pooled across paintings and observers could be described by a Gaussian function centered at 10o, i.e. very close to the original colors of the paintings. This result suggest that painters are able to predict well what compositions of colors observers prefer.
| Original language | English |
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| Title of host publication | Volume 9016 of the series Lecture Notes in Computer Science, Computational Color Imaging |
| Place of Publication | Switzerland |
| Publisher | Springer Nature |
| Pages | 236-242 |
| Number of pages | 7 |
| Publication status | Published - Mar 2015 |
| Event | 5th Computational Color Imaging Workshop - Saint Etienne, France Duration: 24 Mar 2015 → 26 Mar 2015 |
Conference
| Conference | 5th Computational Color Imaging Workshop |
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| City | Saint Etienne, France |
| Period | 24/03/15 → 26/03/15 |
Keywords
- Colors of paintings, Color vision, Art visualization, Color rendering, Aesthetics