August 2014
Volume 14, Issue 10
Vision Sciences Society Annual Meeting Abstract  |   August 2014
Perception of saturation in natural scenes
Author Affiliations
  • Florian Schiller
    Department of Psychology, Justus-Liebig-University Giessen
  • Karl Gegenfurtner
    Department of Psychology, Justus-Liebig-University Giessen
Journal of Vision August 2014, Vol.14, 465. doi:
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      Florian Schiller, Karl Gegenfurtner; Perception of saturation in natural scenes. Journal of Vision 2014;14(10):465. doi:

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      © ARVO (1962-2015); The Authors (2016-present)

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For most color spaces, there is at least one measure for determining the saturation of a color. It is unclear, how well these different measures correspond to human perception. We conducted two experiments in an attempt to fill this gap. We chose 80 color images of natural scenes from the categories "flowers", "man-made", "foliage", and "land-water" from the McGill database of calibrated color images. The images were shown to 8 participants in full color and to another 8 participants in grayscale on a calibrated LCD monitor in randomized order. Participants were asked to select the pixel in the image that appeared to be the most saturated with a mouse cursor. We compared the judgments of the participants to different measures of saturation defined in the DKL, LAB, LUV, and xyY color spaces. We also used saturation from the HSV color space and a measure defined by Koenderink. Our results show that all of the measures capture saturation quite well. The pixels chosen by the participants from the color images were amongst the top 20% saturated pixels for all of the measures, and amongst the top 10% when a small degree of spatial uncertainty with respect to the chosen pixel was allowed. When confronted with the grayscale images, participants were still able to pick pixels whose counterparts in the color images were rated as more saturated by the six measures than randomly selected pixels. Our results indicate that saturation in natural scenes can be specified quite well even without taking image structure into account. Participants are able to infer saturation from the grayscale images from features correlated with color saturation or using prior knowledge in order to make their judgments.

Meeting abstract presented at VSS 2014


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