Automatic, Fast and Perceptually Accurate Gamut Mapping Based on Vision Science Models

A testing paradigm for quantifying ICC profilers

Color and Imaging Conference ◽

10.2352/issn.2169-2629.2019.27.15 ◽

2019 ◽

Vol 2019 (1) ◽

pp. 80-85

Author(s):

Pooshpanjan Roy Biswas ◽

Alessandro Beltrami ◽

Joan Saez Gomez

Keyword(s):

Digital Camera ◽

Visual Assessment ◽

Just Noticeable Difference ◽

Color Management ◽

Color Gamut ◽

Gamut Mapping ◽

Digital File ◽

Mapping Process ◽

Testing Paradigm

To reproduce colors in one system which differs from another system in terms of the color gamut, it is necessary to use a color gamut mapping process. This color gamut mapping is a method to translate a specific color from a medium (screen, digital camera, scanner, digital file, etc) into another system having a difference in gamut volume. There are different rendering intent options defined by the International Color Consortium [5] to use the different reproduction goals of the user [19]. Any rendering intent used to reproduce colors, includes profile engine decisions to do it, i.e. looking for color accuracy, vivid colors or pleasing reproduction of images. Using the same decisions on different profile engines, the final visual output can look different (more than one Just Noticeable Difference[16]) depending on the profile engine used and the color algorithms that they implement. Profile performance substantially depends on the profiler engine used to create them. Different profilers provide the user with varying levels of liberty to design a profile for their color management needs and preference. The motivation of this study is to rank the performance of various market leading profiler engines on the basis of different metrics designed specifically to report the performance of particular aspects of these profiles. The study helped us take valuable decisions regarding profile performance without any visual assessment to decide on the best profiler engine.

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Opponent Center-Surround Contrast: Colour to Grey Conversion

London Imaging Meeting ◽

10.2352/issn.2694-118x.2020.lim-18 ◽

2020 ◽

Vol 2020 (1) ◽

pp. 105-108

Author(s):

Ali Alsam

Keyword(s):

Evolutionary Algorithms ◽

Three Dimensional ◽

The State ◽

Colour Space ◽

Vision Science ◽

State Of Art ◽

Over Time

Vision is the science that informs us about the biological and evolutionary algorithms that our eyes, opticnerves and brains have chosen over time to see. This article is an attempt to solve the problem of colour to grey conversion, by borrowing ideas from vision science. We introduce an algorithm that measures contrast along the opponent colour directions and use the results to combine a three dimensional colour space into a grey. The results indicate that the proposed algorithm competes with the state of art algorithms.

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P‐13.11: A Color Gamut Mapping Reducing the Influence of IR‐drop in OLED Displays

SID Symposium Digest of Technical Papers ◽

10.1002/sdtp.13716 ◽

2019 ◽

Vol 50 (S1) ◽

pp. 985-987

Author(s):

Junting Ouyang ◽

Bojia Lyu ◽

DZ Peng ◽

Kang Yang ◽

Xiangzi Kong ◽

...

Keyword(s):

Color Gamut ◽

Gamut Mapping ◽

Ir Drop

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A practical metric for melanopic metrology

Lighting Research and Technology ◽

10.1177/1477153518824147 ◽

2019 ◽

Vol 51 (8) ◽

pp. 1178-1191 ◽

Cited By ~ 4

Author(s):

SM Berman ◽

RD Clear

Keyword(s):

Ganglion Cell ◽

Retinal Ganglion Cell ◽

Vision Research ◽

Spectral Response ◽

Retinal Ganglion ◽

Standard Definition ◽

Standard Unit ◽

Vision Science ◽

The Past ◽

Definition Of

Over the past decade, there has been a growing interest in lighting research on the effects of the recently discovered melanopsin receptor (also referred to as the intrinsically photosensitive retinal ganglion cell) and its impacts on health and vision. Presently, there is not a generally accepted metrology for dealing with the spectral response of the melanopsin receptor as applied to both lighting and vision research. A proposition to handle this issue from a vision science perspective has been presented in 2014 in the journal Trends in Neurosciences and from a more lighting perspective in 2017 in Lighting Research and Technology. These propositions are complex, and do not retain the CIE standard definition of a lumen. In this paper, we propose an approach based on effective watts and melanopic/photopic ratios that is both simpler and more closely aligned with CIE standard unit definitions. In addition, we include some practical examples of how such ratios are accessible now, and can be used for both lighting and vision research as well as applications.

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