&lt;title&gt;Categorical color mapping for gamut mapping: II. Using block average image&lt;/title&gt;

Motomura, Hideto

doi:10.1117/12.334549

Cited by 3 publications

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“…Psychophysical colour‐naming experiments offer the most direct and legitimate method of determining the mapping between colour names and the corresponding regions of perceptual colour spaces. Over recent years, colour naming data derived from such experiments has been used for image processing, computer vision and gamut mapping …”

Section: Introductionmentioning

confidence: 99%

Augmenting basic colour terms in english

Mylonas

MacDonald

2015

Color Research & Application

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In an unconstrained colour naming experiment conducted over the web, 330 participants named 600 colour samples in English. The 30 most frequent monolexemic colour terms were analyzed with regards to frequency, consensus among genders, response times, consistency of use, denotative volume in the Munsell and OSA colour spaces and inter-experimental agreement. Each of these measures served for ranking colour term salience; rankings were then combined to give a composite index of basicness. The results support the extension of English inventory from the 11 basic colour terms of Berlin and Kay to 13 terms by the addition of lilac and turquoise.

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Section: Introductionmentioning

confidence: 99%

Augmenting basic colour terms in english

Mylonas

MacDonald

2015

Color Research & Application

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“…where µ i is an average vector, ∑ i the covariance matrix (subscript i = 1, 2, 3, ..., 9) is the number that indicates a color name, and X is a test color vector. The average vector and covariance matrix are derived from a color-naming experiment.…”

Section: Introductionmentioning

confidence: 99%

Analysis of gamut mapping algorithms from the viewpoint of color name matching

Motomura

2002

J Soc Info Display

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The validity of color name matching in analyzing gamut mapping algorithms is described in this paper. The performances of three types of gamut mapping algorithms (cusp scaling, clipping, and categorical weighting) are evaluated in a psychophysical experiment from the viewpoint of color name matching. Based on this evaluation, we have found that cusp-scaling algorithm is capable of controlling lightness reasonably well, compensating for a decrease in chroma caused by the difference in the size of device gamut between a source and a destination device. Ranking of reproducible performance by the three gamut mapping algorithms in the psychophysical experiment is arrived at on the basis of color name matching. Color-naming modelA color-naming model was introduced to obtain a color name for any arbitrary color. The following color names were used in this paper: (1) achromatic, (2) red, (3) brown, (4) pink, (5) orange, (6) yellow, (7) green, (8) blue, and (9) purple. These color names were selected from 11 categorical basic color names. 2 In this paper, white, black, and gray are referred to as the achromatic. Matching a given test color to one the nine color names is described by Mahalano-

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General-Purpose Gamut-Mapping Algorithms: Evaluation of Contrast-Preserving Rescaling Functions for Color Gamut Mapping

Braun¹,

Fairchild²

1999

cic

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<title>Categorical color mapping for gamut mapping: II. Using block average image</title>

Cited by 3 publications

References 0 publications

Augmenting basic colour terms in english

Augmenting basic colour terms in english

Analysis of gamut mapping algorithms from the viewpoint of color name matching

General-Purpose Gamut-Mapping Algorithms: Evaluation of Contrast-Preserving Rescaling Functions for Color Gamut Mapping

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