A complete colorimetric characterization of a cathode ray tube (CRT) display was generated using an all-visual psychophysical measurement procedure. This process requires no external props or matching stimuli and uses a three-stage approach for estimating the display’s (1) system nonlinearities (i.e., gamma), (2) channel luminance ratios (i.e., white-point setting), and (3) phosphor chromaticity matrix. Simple homochromatic brightness matching experiments are used to solve the system nonlinearities using either spatially or temporally modulated stimuli. A novel heterochromatic minimum-flicker technique is presented for estimating the relative luminance contributions of each display channel. Finally, a chromaticity selection process is used to choose an appropriate chromaticity set for the display. These components are used to populate a model of the colorimetric mixing characteristics of the display device. The visually populated display model was compared to an instrumentation-based process. The results of this analysis showed that the all-visual characterization process compared favorably to the instrumentation-based model. For 12 subjects, the mean colorimetric errors (averaged across subjects) for a typical color-management task were approximately 2 CIE ΔE94 units with maximum colorimetric errors (averaged across subjects) of approximately 4 CIE ΔE94 units.
In color gamut mapping of pictorial images, the lightness rendition of the mapped images plays a major role in the quality of the final image. For color gamut mapping tasks, where the goal is to produce a match to the original scene, it is important to maintain the perceived lightness contrast of the original image. Typical lightness remapping functions such as linear compression, soft compression, and hard clipping reduce the lightness contrast of the input image. Sigmoidal remapping functions were utilized to overcome the natural loss in perceived lightness contrast that results when an image from a full dynamic range device is scaled into the limited dynamic range of a destination device. These functions were tuned to the particular lightness characteristics of the images used and the selected dynamic ranges. The sigmoidal remapping functions were selected based on an empirical contrast enhancement model that was developed for the result of a psychophysical adjustment experiment. The results of this study showed that it was possible to maintain the perceived lightness contrast of the images by using sigmoidal contrast enhancement functions to selectively rescale images from a source device with a full dynamic range into a destination device with a limited dynamic range.