Gamut reduction transforms the colors of an input image
within the range of a target device. A good gamut reduction algorithm
will preserve the experience felt by the viewer of the original
image. Saliency algorithms predict the image regions where an
observer first focuses. Therefore, there exists a connection between
both concepts since modifying the saliency of the image
will modify the viewer’s experience. However, very little attention
has been given to relate saliency and gamut mapping. ...
Gamut reduction transforms the colors of an input image
within the range of a target device. A good gamut reduction algorithm
will preserve the experience felt by the viewer of the original
image. Saliency algorithms predict the image regions where an
observer first focuses. Therefore, there exists a connection between
both concepts since modifying the saliency of the image
will modify the viewer’s experience. However, very little attention
has been given to relate saliency and gamut mapping. In this
paper we propose to modify a recent gamut reduction algorithm
proposed by Zamir et al. [32] in order to better respect the saliency
of the original image in the reproduced one. Our results show that
the proposed approach presents a gamut-mapped image whose
saliency map is closer to that of the original image with a minor
loss in the accuracy of perceptual reproduction.
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