Brightness scaling according to gamut relativity

Vladusich, Tony

doi:10.1002/col.21823

Cited by 4 publications

(9 citation statements)

References 18 publications

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“…The restatement of several extant computational problems of visual surface representation in terms of gamut relativity provides a new perspective on the nature of computational vision [53][54][55][56]. Rather than postulating a reified mapping from physical dimensions to perceptual dimensions, gamut relativity suggests how properties such as lightness, transparency, and gloss emerge naturally through a process of vector computation operating in blackness-whiteness space.…”

Section: Discussionmentioning

confidence: 99%

“…The model is framed within the context of a broader computational theory, termed gamut relativity, that has previously been used to explain a wide body of psychophysical data on brightness, lightness, and transparency perception involving diffusely reflecting surfaces [53][54][55][56]. Here I extend the theory to the domain of specularly reflecting surfaces in order to explain key aspects of gloss and lightness perception in a unified manner.…”

Section: Introductionmentioning

confidence: 99%

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A unified account of gloss and lightness perception in terms of gamut relativity

Vladusich

2013

J. Opt. Soc. Am. A

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A recently introduced computational theory of visual surface representation, termed gamut relativity, overturns the classical assumption that brightness, lightness, and transparency constitute perceptual dimensions corresponding to the physical dimensions of luminance, diffuse reflectance, and transmittance, respectively. Here I extend the theory to show how surface gloss and lightness can be understood in a unified manner in terms of the vector computation of "layered representations" of surface and illumination properties, rather than as perceptual dimensions corresponding to diffuse and specular reflectance, respectively. The theory simulates the effects of image histogram skewness on surface gloss/lightness and lightness constancy as a function of specular highlight intensity. More generally, gamut relativity clarifies, unifies, and generalizes a wide body of previous theoretical and experimental work aimed at understanding how the visual system parses the retinal image into layered representations of surface and illumination properties.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A unified account of gloss and lightness perception in terms of gamut relativity

Vladusich

2013

J. Opt. Soc. Am. A

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“…A lighter surround has a very great impact on the appearance of a dark region, while a darker surround has a much smaller impact on the appearance of a light region (Heinemann, 1972; Rudd and Zemach, 2004, 2005, 2007; Vladusich, 2013a,b). Gilchrist (1988) measured the relative influences of dark and light surrounds on identical gray papers in a simultaneous contrast display and concluded that most of the illusion was accounted for by darkness induction from the light surround to the decremental paper.…”

Section: The Basic Neural Edge-integration Modelmentioning

confidence: 99%

A cortical edge-integration model of object-based lightness computation that explains effects of spatial context and individual differences

Rudd

2014

Front. Hum. Neurosci.

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Previous work has demonstrated that perceived surface reflectance (lightness) can be modeled in simple contexts in a quantitatively exact way by assuming that the visual system first extracts information about local, directed steps in log luminance, then spatially integrates these steps along paths through the image to compute lightness (Rudd and Zemach, 2004, 2005, 2007). This method of computing lightness is called edge integration. Recent evidence (Rudd, 2013) suggests that human vision employs a default strategy to integrate luminance steps only along paths from a common background region to the targets whose lightness is computed. This implies a role for gestalt grouping in edge-based lightness computation. Rudd (2010) further showed the perceptual weights applied to edges in lightness computation can be influenced by the observer's interpretation of luminance steps as resulting from either spatial variation in surface reflectance or illumination. This implies a role for top-down factors in any edge-based model of lightness (Rudd and Zemach, 2005). Here, I show how the separate influences of grouping and attention on lightness can be modeled in tandem by a cortical mechanism that first employs top-down signals to spatially select regions of interest for lightness computation. An object-based network computation, involving neurons that code for border-ownership, then automatically sets the neural gains applied to edge signals surviving the earlier spatial selection stage. Only the borders that survive both processing stages are spatially integrated to compute lightness. The model assumptions are consistent with those of the cortical lightness model presented earlier by Rudd (2010, 2013), and with neurophysiological data indicating extraction of local edge information in V1, network computations to establish figure-ground relations and border ownership in V2, and edge integration to encode lightness and darkness signals in V4.

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“…The cornerstone of their argument is that neutral scale is determined by background luminance, not by background appearance, contrary to the implication of the differential equation generating the power law. Vladusich takes a more general view, associating power‐law scaling of luminance with cortical processing (e.g. estimating magnitude of light intensity, inferring the illuminant) and identifying logarithmic scaling with retinal discriminations (e.g.…”

Section: Discussionmentioning

confidence: 99%

Calculation of self-luminous neutral scale: how many neutral steps can you see on that display?

Carter

Brill²

2014

Jnl Soc Info Display

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One of the dimensions of visual perception usable for information display is neutral scale, the achromatic aspect of colour. An existing neutral scale calculation, CIE L*, relates reflected luminance to lightness magnitude and discrimination. But there is no similar international standard calculation for self‐luminous displays. In this article, we introduce a candidate self‐luminous neutral‐scale calculation, to complement CIE L*. We particularly examine its low‐contrast gain, the degree of crispening. This low‐contrast gain is a function of the background luminance, from which the target contrast is an increment or decrement. We demonstrate the use of the calculation to predict matches of neutral scale across different background luminances, and to model the successive thresholds of the Digital Imaging and Communications in Medicine Gray Scale Display Function. Finally, the proposed self‐luminous neutral scale is calculated to show the maximum number of discriminable differences, and the associated optimum background luminance, in representative ranges of display luminance.

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Brightness scaling according to gamut relativity

Cited by 4 publications

References 18 publications

A unified account of gloss and lightness perception in terms of gamut relativity

A unified account of gloss and lightness perception in terms of gamut relativity

A cortical edge-integration model of object-based lightness computation that explains effects of spatial context and individual differences

Calculation of self-luminous neutral scale: how many neutral steps can you see on that display?

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