Gamut mapping in a high-dynamic-range color space

2014 ◽  
Author(s):  
Jens Preiss ◽  
Mark D. Fairchild ◽  
James A. Ferwerda ◽  
Philipp Urban
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].


2014 ◽  
Author(s):  
Jiancheng Zhang ◽  
Xiaohua Liu ◽  
Liquan Dong ◽  
Yuejin Zhao ◽  
Ming Liu

2020 ◽  
Vol 2020 (9) ◽  
pp. 214-1-214-9
Author(s):  
Anustup Choudhury ◽  
Scott Daly

There are an increasing number of databases describing subjective quality responses for HDR (high dynamic range) imagery with various distortions. The dominant distortions across the databases are those that arise from video compression, which are primarily perceived as achromatic, but there are some chromatic distortions due to 422 and other chromatic sub-sampling. Tone mapping from the source HDR levels to various levels of reduced capability SDR (standard dynamic range) are also included in these databases. While most of these distortions are achromatic, tone-mapping can cause changes in saturation and hue angle when saturated colors are in the upper hull of the of the color space. In addition, there is one database that specifically looked at color distortions in an HDR-WCG (wide color gamut) space. From these databases we can test the improvements to well-known quality metrics if they are applied in the newly developed color perceptual spaces (i.e., representations) specifically designed for HDR and WCG. We present results from testing these subjective quality databases to computed quality using the new color spaces of Jzazbz and ICTCP, as well as the commonly used SDR color space of CIELAB.


2017 ◽  
Vol 25 (13) ◽  
pp. 15131 ◽  
Author(s):  
Muhammad Safdar ◽  
Guihua Cui ◽  
Youn Jin Kim ◽  
Ming Ronnier Luo

2017 ◽  
Vol 2017 (18) ◽  
pp. 48-59
Author(s):  
Mekides Abebe ◽  
Tania Pouli ◽  
Mohamed-Chaker Larabi

2011 ◽  
Vol 61 (5) ◽  
pp. 462 ◽  
Author(s):  
Om Prakash Verma ◽  
V. K. Madasu ◽  
V. Shantaram

<p>High dynamic range images contain both the underexposed and the overexposed regions. The enhancement of the underexposed and the overexposed regions is the main concern of this paper. Two new transformation functions are proposed to modify the fuzzy membership values of under and the overexposed regions of an image respectively.For the overexposed regions, a rectangular hyperbolic function is used while for the underexposed regions, an S-function is applied. The shape and range of these functions can be controlled by the parameters involved, which are optimized using the bacterial foraging optimization algorithm so as to obtain the enhanced image. The hue, saturation, and intensity (HSV) color space is employed for the purpose of enhancement, where the hue component is preserved to keep the original color composition intact. This approach is applicable to a degraded image of mixed type. On comparison, the proposed transforms yield better results than the existing transformation functions17 for both the underexposed and the overexposed regions.</p><p><strong>Defence Science Journal, 2011, 61(5), pp.462-472</strong><strong><strong>, DOI:http://dx.doi.org/10.14429/dsj.61.1184</strong></strong></p>


2019 ◽  
Vol 29 (7) ◽  
pp. 2055-2066 ◽  
Author(s):  
Ratnajit Mukherjee ◽  
Kurt Debattista ◽  
Thomas-Bashford Rogers ◽  
Maximino Bessa ◽  
Alan Chalmers

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