How many object colors can we distinguish?

Kuehni, Rolf G.

doi:10.1002/col.21980

Cited by 23 publications

(12 citation statements)

References 16 publications

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“…Normal trichromats are able to differentiate more than 2 million colors (Pointer and Attridge, 1998 ; Kuehni, 2016 ) which can be clustered in a greatly reduced number of color categories. This reduction happens because thousands of colors differing in lightness and/or chroma and/or hue can belong to a single category and, consequently, be denoted by the same term.…”

Section: Introductionmentioning

confidence: 99%

Basic Color Terms (BCTs) and Categories (BCCs) in Three Dialects of the Spanish Language: Interaction Between Cultural and Universal Factors

et al. 2018

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Two experiments were performed to identify and compare the Basic Color Terms (BCTs) and the Basic Color Categories (BCCs) included in three dialects (Castilian, Mexican, and Uruguayan) of the Spanish language. Monolexemic Elicited lists were used in the first experiment to identify the BCTs of each dialect. Eleven BCTs appeared for the Spanish and the Mexican, and twelve did so for the Uruguayan. The six primary BCTs (rojo “red,” verde “green,” amarillo “yellow,” azul “blue,” negro “black,” and blanco “white”) appeared in the three dialects. This occurred for only three derived BCTs (gris “gray,” naranja “orange,” and rosa “pink”) but not for the other five derived BCTs (celeste “sky blue,” marrón “brown,” café “brown,” morado “purple,” and violeta “purple”). Color transitions were used in the second experiment for two different tasks. Extremes naming task was used to determine the relation between two different dialects' BCTs: equality, equivalence or difference. The results provided the first evidence for marrón “brown” and café “brown” being equivalent terms for the same BCC (brown in English) as is the case of morado “purple” and violeta “purple.” Uruguayan celeste “sky blue” had no equivalent BCT in the other two dialects. Boundary delimitation task required the selection of the color in the boundary between two categories. The task was used to reasonably estimate the volume occupied by each BCC in the color space considering its chromatic area and lightness range. Excluding sky blue (celeste “sky blue”) and blue (azul “blue”), the other BCCs color volumes were similar across the three dialects. Uruguayan sky blue and blue volumes conjointly occupied the portion of the color space corresponding to the Castilian and Mexican blue BCC. The fact that the BCT celeste “sky blue” only appeared in Uruguayan very probably derived from specific cultural factors (the use of the color in the flags and the arrival of an important number of Italian immigrants). Nevertheless, these cultural factors seem to nurture from a perceptive structuring of the color space, which nature is universal, as the boundaries of this category can be delimited from the responses of Spanish and Mexican participants.

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Section: Introductionmentioning

confidence: 99%

Basic Color Terms (BCTs) and Categories (BCCs) in Three Dialects of the Spanish Language: Interaction Between Cultural and Universal Factors

et al. 2018

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“…Michael Brill, in his commentary on my article “How many object colors can we distinguish?” takes a different approach in determining this number. I based my hypothesis on the methodology used by Masaoka et al .…”

Section: Commentsmentioning

confidence: 99%

Comments: Number of perceivable object color stimuli

Kuehni¹

2016

Color Research & Application

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M. Brill, in his comments “Maximum number of discriminable colors in a region of uniform color space,” offers a different calculation method from that used by R. G. Kuehni in “How many object colors can we distinguish?,” one based on close‐packing of just noticeable difference spheres. The number per just noticeable difference (JND) sphere is lower than that derived in Kuehni's study. Based on the resulting number of close‐packed JND spheres in the CIECAM02/D65 object color solid and Brill's described multiplier of 5.923 potential stimuli within a JND sphere, the resulting number of distinguishable color stimuli is 9.114 million. © 2016 Wiley Periodicals, Inc. Col Res Appl, 00, 000–000, 2016

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“…The u′v′ gamut area metric is also popular among display manufacturers because u′v′ area ratios are typically larger than xy area ratios . In a scientific sense, however, the gamut calculation must be volumetric in a perceptually uniform color space, rather than planimetric in a chromaticity diagram.…”

Section: Introductionmentioning

confidence: 99%

Analysis of standard chromaticity gamut area metrics

Masaoka¹

2016

J Soc Info Display

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It is herein proposed to measure display gamut sizes by employing the International Telecommunication Union-Radiocommunication Sector Recommendation BT.2020 (Rec. 2020) area-coverage ratios in the xy chromaticity diagram rather than the standard gamut area metrics that use the horseshoe-shaped spectrum chromaticity area as the target in the u 0 v 0 chromaticity diagram. It is more reasonable to use the Rec. 2020 gamut than the spectrum gamut as the target because the Rec. 2020 area-coverage ratios in the xy diagram are better correlated than the spectrum areacoverage ratios with the volume-coverage ratios of object color gamuts that are visually significant in displaying natural scenes.

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How many object colors can we distinguish?

Abstract: The question of how many different colors humans can perceive has been of interest to philosophers, psychologists and color scientists for centuries.

Cited by 23 publications

References 16 publications

Basic Color Terms (BCTs) and Categories (BCCs) in Three Dialects of the Spanish Language: Interaction Between Cultural and Universal Factors

Basic Color Terms (BCTs) and Categories (BCCs) in Three Dialects of the Spanish Language: Interaction Between Cultural and Universal Factors

Comments: Number of perceivable object color stimuli

Analysis of standard chromaticity gamut area metrics

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