Colour gamut mapping using vividness scale

2019 ◽  
Vol 2019 (14) ◽  
pp. 79-1-79-7
Author(s):  
Baiyue Zhao ◽  
Lihao Xu ◽  
Ming Ronnier Luo
Keyword(s):  
2016 ◽  
Vol 2016 (5) ◽  
pp. 102-106
Author(s):  
Xiaoying Shen ◽  
Cheng Ye ◽  
Zhen Liu ◽  
Guangyuan Wu

2018 ◽  
Vol 26 (9) ◽  
pp. 11481 ◽  
Author(s):  
Lihao Xu ◽  
Baiyue Zhao ◽  
M. R. Luo
Keyword(s):  

2021 ◽  
Vol 2021 (29) ◽  
pp. 71-76
Author(s):  
Xu Lihao ◽  
Xu Qiang ◽  
Ming Ronnier Luo

This paper describes a colour image enhancement method for those having colour-vision deficiencies. The proposed method can be divided into 3 stages. Firstly, a conversion relation between the wavelength shift (measured in nanometers) of a colour deficient observer (CDO) and the severity of colour deficiency was established. Secondly, the perceived colour gamut was built by applying the conversion relation. Finally, the original images were re-coloured by adopting a gamut mapping algorithm to map colours from the gamut of colour normal observer (CNO) to that of a CDO. Psychophysical experiments were then conducted to show the effectiveness of the method.


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.


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