Gamut Mapping

2021 ◽  
pp. 499-507
Author(s):  
Rajeev Ramanath ◽  
Mark S. Drew
Keyword(s):  
2019 ◽  
Vol 2019 (1) ◽  
pp. 80-85
Author(s):  
Pooshpanjan Roy Biswas ◽  
Alessandro Beltrami ◽  
Joan Saez Gomez

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.


2019 ◽  
Vol 50 (S1) ◽  
pp. 985-987
Author(s):  
Junting Ouyang ◽  
Bojia Lyu ◽  
DZ Peng ◽  
Kang Yang ◽  
Xiangzi Kong ◽  
...  
Keyword(s):  

2007 ◽  
Author(s):  
Min-Ki Cho ◽  
Heui-Keun Choh ◽  
Se-Eun Kim ◽  
Yun-Tae Kim ◽  
Yousun Bang
Keyword(s):  

2012 ◽  
Vol 262 ◽  
pp. 36-39 ◽  
Author(s):  
Yun Hui Luo ◽  
Mao Hai Lin

As color gamut of digital output device greatly affects image appearance, accurate and effective gamut description for output device is intensively required for developing high-quality image reproduction technique based on gamut mapping. In this paper, we present a novel method to determine color gamut of output device by using a specific 3D reconstruction technology and device ICC profile. First, we populate the device color space by uniform sampling in the RGB 3-Dimensional space, and convert these sampling points to CMYK color space. Then, we work out the CIE LAB value of these points according to the ICC profile of output device. At last, in CIE LAB color space the boundary of these points is determined by using a gamut boundary descriptor based on Ball-Pivoting Algorithm (BPA) proposed by Bernardini. Compared with the results generated by ICC3D, our proposed method can compute device gamut more efficiently and at the same time give a more accurate gamut description of the output device. It will be help to develop effective gamut mapping algorithms for color reproduction.


2021 ◽  
Vol 2021 (3) ◽  
pp. 108-1-108-14
Author(s):  
Eberhard Hasche ◽  
Oliver Karaschewski ◽  
Reiner Creutzburg

In modern moving image production pipelines, it is unavoidable to move the footage through different color spaces. Unfortunately, these color spaces exhibit color gamuts of various sizes. The most common problem is converting the cameras’ widegamut color spaces to the smaller gamuts of the display devices (cinema projector, broadcast monitor, computer display). So it is necessary to scale down the scene-referred footage to the gamut of the display using tone mapping functions [34].In a cinema production pipeline, ACES is widely used as the predominant color system. The all-color compassing ACES AP0 primaries are defined inside the system in a general way. However, when implementing visual effects and performing a color grade, the more usable ACES AP1 primaries are in use. When recording highly saturated bright colors, color values are often outside the target color space. This results in negative color values, which are hard to address inside a color pipeline. "Users of ACES are experiencing problems with clipping of colors and the resulting artifacts (loss of texture, intensification of color fringes). This clipping occurs at two stages in the pipeline: <list list-type="simple"> <list-item>- Conversion from camera raw RGB or from the manufacturer’s encoding space into ACES AP0</list-item> <list-item>- Conversion from ACES AP0 into the working color space ACES AP1" [1]</list-item> </list>The ACES community established a Gamut Mapping Virtual Working Group (VWG) to address these problems. The group’s scope is to propose a suitable gamut mapping/compression algorithm. This algorithm should perform well with wide-gamut, high dynamic range, scene-referred content. Furthermore, it should also be robust and invertible. This paper tests the behavior of the published GamutCompressor when applied to in- and out-ofgamut imagery and provides suggestions for application implementation. The tests are executed in The Foundry’s Nuke [2].


Author(s):  
Cristian Munteanu ◽  
Agostinho Rosa ◽  
Manuel Galan ◽  
Enrique Rubio Royo

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