Paper
1 May 1994 Use of a model of the human visual system to determine the attributes of aperture colors: hue, brightness, and saturation
Robert Siminoff
Author Affiliations +
Proceedings Volume 2179, Human Vision, Visual Processing, and Digital Display V; (1994) https://doi.org/10.1117/12.172688
Event: IS&T/SPIE 1994 International Symposium on Electronic Imaging: Science and Technology, 1994, San Jose, CA, United States
Abstract
A previously developed model of the human fovea is modified for analysis of colored stimuli. Psychophysical data on perception of aperture colors are used as a guide for the development of color perception by the model. The channels carrying information about color are represented by the summed responses of midget C- and L-type cells. The spectral energy distribution of any colored stimulus produces within the cell types a unique pattern of activities from which the amounts of each of the hues, whiteness, brightness, and saturation can be determined. The present analysis is restricted to the blue-cone region of the parafovea, which surrounds the blue-cone-free central fovea. For aberration-free dispersion and for no or little self-adaptation, the psychophysical and model data are in good agreement as to the 3 attributes hue, brightness, and saturation. As a result of univariance, information as to the spectral distribution of the stimulus cannot be used, and this creates a problem as how to normalize colored stimuli to a common set of standards. A method based on normalization of the outputs of the retinal cells to a common white is presented.
© (1994) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Robert Siminoff "Use of a model of the human visual system to determine the attributes of aperture colors: hue, brightness, and saturation", Proc. SPIE 2179, Human Vision, Visual Processing, and Digital Display V, (1 May 1994); https://doi.org/10.1117/12.172688
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KEYWORDS
Cones

Data modeling

Colorimetry

Visual process modeling

Visual system

Color vision

Image compression

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