Geometric relations between scales of small colour differences

Witt, Klaus

doi:10.1002/(sici)1520-6378(199904)24:2<78::aid-col3>3.3.co;2-d

Cited by 28 publications

(58 citation statements)

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“…To estimate the relationship between discrimination thresholds in CIELAB and DIN99d, a color discrimination dataset with small-magnitude color differences (SCDs) was used. The set included four sub-datasets: BFD-P [61], RIT-DuPont [62], Leeds [63], and Witt [64], with 2776, 156, 307, and 418 pairs, respectively, and an average of 2.5⌬E * ab [52]. A weighting factor was applied to each individual dataset to compensate for the relative differences in number of samples.…”

Section: Discussionmentioning

confidence: 99%

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Number of discernible colors for color-deficient observers estimated from the MacAdam limits

et al. 2010

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We estimated the number of colors perceived by color normal and color-deficient observers when looking at the theoretic limits of object-color stimuli. These limits, the optimal color stimuli, were computed for a color normal observer and CIE standard illuminant D65, and the resultant colors were expressed in the CIELAB and DIN99d color spaces. The corresponding color volumes for abnormal color vision were computed using models simulating for normal trichromatic observers the appearance for dichromats and anomalous trichomats. The number of colors perceived in each case was then computed from the color volumes enclosed by the optimal colors also known as MacAdam limits. It was estimated that dichromats perceive less than 1% of the colors perceived by normal trichromats and that anomalous trichromats perceive 50%-60% for anomalies in the medium-wavelength-sensitive and 60%-70% for anomalies in the long-wavelength-sensitive cones. Complementary estimates obtained similarly for the spectral locus of monochromatic stimuli suggest less impairment for color-deficient observers, a fact that is explained by the two-dimensional nature of the locus.

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Section: Discussionmentioning

confidence: 99%

“…A linear fit to the data gives a slope of 0.6, and this was the value used for discrimination threshold in DIN99d. [62], Leeds [63], and Witt [64] expressed as a function of the corresponding color differences expressed in the CIELAB color space.…”

Section: Discussionmentioning

confidence: 99%

Number of discernible colors for color-deficient observers estimated from the MacAdam limits

et al. 2010

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“…Among the different colour spaces, a well-known one, specially in computer graphics, is the Hue-Chroma-Lightness (HCL) colour space [23] A series of experimental datasets: BFD-P, Leeds, RIT-Dupont, and Witt, which are combined to form the COM dataset, have been obtained in order to characterize the perceptual difference between pairs of colour samples [1,24,26,27,57,58]. In these datasets each pair of colour samples is associated with a value ∆V which represents the experimental perceptual difference between them.…”

Section: Obviously In This Case F Satisfies (3)mentioning

confidence: 99%

Some questions in fuzzy metric spaces

Gregori

MiòAna

Morillas

2012

Fuzzy Sets and Systems

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ElsevierGregori Gregori, V.; Morillas Gómez, S.; Miñana Prats, JJ. (2012) AbstractThe George and Veeramani's fuzzy metric defined by M * (x, y, t) = min{x,y}+t max{x,y}+t on [0, ∞[ (the set of non-negative real numbers) has shown some advantages in front of classical metrics in the process of filtering images. In this paper we study from the mathematical point of view this fuzzy metric and other fuzzy metrics related to it. As a consequence of this study we introduce, throughout the paper, some questions relative to fuzzy metrics. Also, as another practical application, we show that this fuzzy metric is useful for measuring perceptual colour differences between colour samples.

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“…The RIT-Dupont dataset provides 312 pairs of colors that have been experimentally determined to be separated by a small and equal perceptual distance [Berns et al 1991]. The Witt dataset provides both small and medium distance data for 418 color pairs [Witt 1999]. …”

Section: Estimating the A Matrixmentioning

confidence: 99%