Orthogonal combination of the three visual channels

Ingling, Carl R.; Tsou, Brian H.

doi:10.1016/0042-6989(77)90013-x

Cited by 223 publications

(71 citation statements)

References 17 publications

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“…For example, the hue and saturation discrimination functions in Figs. 1a and 2a can be computed by modeling combinations of the outputs of cone classes or color channels (r-g, y-b, luminance) to small variations in wavelength or color purity (9)(10)(11)(19)(20)(21). Similarly, some studies have successfully rationalized the Hunt, Abney, and Bezold-Brücke effects as nonlinear combination of cone (9) or color channel responses (21).…”

Section: Discussionmentioning

confidence: 99%

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Spectral statistics in natural scenes predict hue, saturation, and brightness

Long

Yang

Purves

2006

Proc. Natl. Acad. Sci. U.S.A.

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The perceptual color qualities of hue, saturation, and brightness do not correspond in any simple way to the physical characteristics of retinal stimuli, a fact that poses a major obstacle for any explanation of color vision. Here we test the hypothesis that these basic color attributes are determined by the statistical covariations in the spectral stimuli that humans have always experienced in typical visual environments. Using a database of 1,600 natural images, we analyzed the joint probability distributions of the physical variables most relevant to each of these perceptual qualities. The cumulative density functions derived from these distributions predict the major colorimetric functions that have been reported in psychophysical experiments over the last century.color ͉ colorimetry ͉ perception ͉ psychophysics ͉ vision C olor percepts are described in terms of hue (the sensation of the relative redness, greenness, blueness, or yellowness of a spectral stimulus), saturation (the degree to which a color percept deviates from neutral gray), and brightness (the apparent intensity of the stimulus). Although color percepts are obviously initiated by the spectral characteristics of light stimuli, a major difficulty rationalizing these color qualities in either neurobiological or psychological terms is that they are not linked in any straightforward way to the physical attributes of the corresponding retinal stimuli (i.e., to the energy distribution in the stimuli, to their relative uniformity, or to their overall power, respectively). Thus varying the physical parameter underlying any one of these perceptual categories changes the appearance of all three qualities in highly nonlinear and interdependent ways (1-8) .These classical psychophysical functions, which are illustrated in the Figs. 1a-6a, can be divided into those measured by color discrimination testing and those revealed in color-matching paradigms. In color discrimination tests, the ability to distinguish equally noticeable (or just noticeable) differences in hue or saturation varies systematically as a function of the wavelength of a monochromatic stimulus (1, 2). In color-matching tests, (i) saturation varies as a function of luminance [the Hunt effect (3) Motivated by evidence that natural image statistics predict the response properties of some visual neurons (12) and that imagesource statistics predict several aspects of visual perception (13-16), we here examine the hypothesis that this perplexing phenomenology arises from a scheme of visual processing based on statistical covariations of the physical characteristics of light stimuli in typical visual environments. To test this possibility, we acquired a database of 1,600 color images of natural scenes to approximate the range of light stimuli that humans have normally witnessed (see Fig. 7, which is published as supporting information on the PNAS web site). The joint probability distributions of the physical correlates most closely associated with hue, saturation, and brightness were then analyz...

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Section: Discussionmentioning

confidence: 99%

“…Most investigators have sought to rationalize colorimetric functions as epiphenomena of early visual processing (see refs. 3,6,[8][9][10][11][19][20][21]; see ref. 22 for a related review).…”

Section: Discussionmentioning

confidence: 99%

Spectral statistics in natural scenes predict hue, saturation, and brightness

Long

Yang

Purves

2006

Proc. Natl. Acad. Sci. U.S.A.

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“…Accepted models for color vision are usually constructed in stages, based on the physiology of human vision [8][9][10][11][12]. Figure 1 shows an schematic diagram that represents a two stage model.…”

Section: Stage Models For Color Visionmentioning

confidence: 99%

“…We begin with the commonly accepted two stage architecture for trichromatic color perception, consisting of a first sensor stage with per-channel gain control and a second opponent encoding stage [8,9]. We adapt this model to dichromats by dropping the non-functional cone from the first stage and replacing the second stage with a transform of suitable dimensionality whose parameters are unknown a priori.…”

Section: Introductionmentioning

confidence: 99%

Dichromatic color perception in a two stage model: Testing for cone replacement and cone loss models

Rodríguez-Pardo

Sharma

2011

2011 IEEE 10th IVMSP Workshop: Perception and Visual Signal Analysis

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We formulate a two stage model of dichromatic color perception that consists of a first sensor layer with gain control followed by an opponent encoding transformation. We propose a method for estimating the unknown parameters in the model by utilizing pre-existing data from psychophysical experiments on unilateral dichromats. The model is validated using this existing data and by using predictions on known test images for detecting dichromacy. Using the model and analysis we evaluate the feasibility of cone loss and cone replacement hypotheses that have previously been proposed for modeling dichromatic color vision. Results indicate that the two stage model offers good agreement with test data. The cone loss and cone replacement models are shown to have fundamental limitations in matching psychophysical observations.Index Terms-Dichromacy, color vision models, stage models, unilateral dichromacy, cone loss hypothesis, cone replacement hypothesis.

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“…However, quite often evaluating colour differences between two samples is more important than obtaining numerical values of perceptual descriptors. Initially, separate models [18][19][20][21][22][23][24] were developed to fit colour appearance and colour difference data. However, the perfect solution would be to obtain a single model, which could reproduce both perceptual descriptors and colour differences.…”

Section: Introductionmentioning

confidence: 99%

Analyzing the metrics of the perceptual space in a new multistage physiological colour vision model

Chorro¹,

Verdú²,

Sáiz³

et al. 2009

Color Research & Application

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In this work, the metric of a new multistage colour vision model, ATTD05, is assessed and a new colour difference formula is suggested. Firstly, the uniformity of the ATTD05 colour space was compared with that of CIECAM02 for some Munsell samples, because if the model yields a uniform perceptual space, we will be able to implement a colour difference formula as a Euclidian distance between two points. Secondly, we developed a new space based on the perceptual descriptors of the model: brightness, hue, colourfulness, and saturation. After that, we calculated the free parameters of the space that better fit the measured and experimental data of two datasets (small-magnitude and large-magnitude colour differences), by minimizing the performance factor (PF/3). Finally, we compare colour differences calculated for ATTD05 and for other models. The PF/3 and the STRESS parameters were used to decide which model predicts better perceptual differences and, therefore, which model was the more uniform.

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Orthogonal combination of the three visual channels

Cited by 223 publications

References 17 publications

Spectral statistics in natural scenes predict hue, saturation, and brightness

Spectral statistics in natural scenes predict hue, saturation, and brightness

Dichromatic color perception in a two stage model: Testing for cone replacement and cone loss models

Analyzing the metrics of the perceptual space in a new multistage physiological colour vision model

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