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Measurement of individual color space using a luminous vector field

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Abstract

This study is intended to measure the geometry of the observer’s color space when viewing a computer screen and to define individual variations from these data. A CIE photometric standard observer assumes that the eye’s spectral efficiency function is constant, and photometry measurements correspond to vectors with fixed directions. By definition, the standard observer decomposes color space into planar surfaces of constant luminance. Using heterochromatic photometry with a minimum motion stimulus, we systematically measure the direction of luminous vectors for many observers and many color points. During the measurement process, the background and stimulus modulation averages are fixed to the given points to ensure that the observer is in a fixed adaptation mode. Our measurements result in a vector field or set of vectors $(x,v)$, where $x$ is the point’s color space position, and $v$ is the observer’s luminosity vector. To estimate surfaces from vector fields, two mathematical hypotheses were used: (1) that surfaces are quadratic or, equivalently, that the vector field model is affine, and (2) that the metric of surfaces is proportional to a visual origin. Across 24 observers, we found that vector fields are convergent and the corresponding surfaces are hyperbolic. The equation of the surface in the display’s color space coordinate system, and in particular the axis of symmetry, varied systematically from individual to individual. A hyperbolic geometry is compatible with studies that emphasize a modification of the photometric vector with changing adaptations.

© 2023 Optica Publishing Group

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Supplementary Material (3)

NameDescription
Dataset 1       Contain Xpert Data 9 x 75 Matrix, 3x75 is X then 6x75 is V for the two observers
Dataset 2       Contain Naive Data 69 x 12 Matrix, 3x12 is X then 66x12 is V for the 22 observers
Supplement 1       Supplementary Method and Results

Data availability

All results data are available in Dataset 1, Ref. [29], and Dataset 2, Ref. [30].

29. D. Alleysson and D. Méary, “DataXprt,” figshare (2023), https://doi.org/10.6084/m9.figshare.21707930.

30. D. Alleysson and D. Méary, “DataNaive,” figshare (2023), https://doi.org/10.6084/m9.figshare.21707933.

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