Psychophysical evaluation of gamut mapping techniques using simple rendered images and artificial gamut boundaries

Abstract

Using a paired comparison paradigm, various gamut mapping algorithms were evaluated using simple rendered images and artificial gamut boundaries. The test images consisted of simple rendered spheres floating in front of a gray background. Using CIELAB as our device-independent color space, cut-off values for lightness and chroma, based on the statistics of the images, were chosen to reduce the gamuts for the test images. The gamut mapping algorithms consisted of combinations of clipping and mapping the original gamut in linear piecewise segments. Complete color space compression in RGB and CIELAB was also tested. Each of the colored originals (R,G,B,C,M,Y, and Skin) were mapped separately in lightness and chroma. In addition, each algorithm was implemented with saturation (C(*)/L(*)) allowed to vary or retain the same values as in the original image. Pairs of test images with reduced color gamuts were presented to twenty subjects along with the original image. For each pair the subjects chose the test image that better reproduced the original. Rank orders and interval scales of algorithm performance with confidence limits were then derived. Clipping all out-of-gamut colors was the best method for mapping chroma. For lightness mapping at low lightness levels and high lightness levels particular gamut mapping algorithms consistently produced images chosen as most like the original. The choice of device-independent color space may also influence which gamut mapping algorithms are best.

ADICT: Accurate Direct and Inverse Color Transformation

“…However, for the linear interpolation method, we also compare with a much denser sampling, 16 3 , as proposed in [9]. Please note that capturing 9 3 and 16 3 images in our experiments with a high-end camera took about 2 hours and 11 hours respectively.…”

“…Please note that capturing 9 3 and 16 3 images in our experiments with a high-end camera took about 2 hours and 11 hours respectively. For projectors in addition to the longer calibration time heating issues also come into picture when we capture 16 3 images. To show the effectiveness of our non-linear parametrization (Section 3.1), we show that a version of our method where the reparametrization is not applied yields less accurate results.…”

“…Out-of-gamut colors can be first mapped to an in-gamut color using gamut mapping techniques [16,17] before applying our method. Fig.…”

Color device calibration: a mathematical formulation

“…Other work has noted that maintaining chroma is of greater importance than maintaining lightness [31]. For detailed gamut mapping experiments, the reader is referred to [28], [29], [32], and [33].…”

“…Typically, a perceptual color space accounts for white point adaptation, color constancy, and other viewing conditions. As before, the patches are measured using a colorimetric device giving for (32) These values are then mapped to the values where maps the CIE values to a perceptual color space such as those found in [41]- [43]. The profile LUT is then constructed between the values and the device control values…”