68.4: Micro-cavity Design of RGBW AMOLED for 100% Color Gamut

2008 ◽  
Vol 39 (1) ◽  
pp. 1050 ◽  
Author(s):  
Baek-woon Lee ◽  
Young In Hwang ◽  
Hae-yeon Lee ◽  
Chi Woo Kim ◽  
Young-gu Ju
Keyword(s):  
Color Gamut ◽  
Cavity Design ◽  
Micro Cavity

scholarly journals Micro-cavity design of bottom-emitting AMOLED with white OLED and RGBW color filters for 100% color gamut

2009 ◽  
Vol 25 (2) ◽  
pp. 36-36
Author(s):  
Baek-woon Lee ◽  
Young-gu Ju ◽  
Young In Hwang ◽  
Hae-yeon Lee ◽  
Chi Woo Kim ◽  
...  
Keyword(s):  
Color Gamut ◽  
Cavity Design ◽  
Micro Cavity ◽  
Color Filters ◽  
White Oled

P-196L: Late-News Poster: Micro-Cavity Design of RGBW AMOLED for Wide Color Gamut and Low Color Shift

2009 ◽  
Vol 40 (1) ◽  
pp. 1752 ◽  
Author(s):  
Baek-woon Lee ◽  
Young In Hwang ◽  
Hae-yeon Lee ◽  
Sungchul Kim ◽  
Sangsoo Kim ◽  
...  
Keyword(s):  
Color Gamut ◽  
Color Shift ◽  
Wide Color Gamut ◽  
Cavity Design ◽  
Late News ◽  
Micro Cavity

Micro-cavity design of bottom-emitting AMOLED with white OLED and RGBW color filters for 100% color gamut

2009 ◽  
Vol 17 (2) ◽  
pp. 151 ◽  
Author(s):  
Baek-woon Lee ◽  
Young-gu Ju ◽  
Young In Hwang ◽  
Hae-yeon Lee ◽  
Chi Woo Kim ◽  
...  
Keyword(s):  
Color Gamut ◽  
Cavity Design ◽  
Micro Cavity ◽  
Color Filters ◽  
White Oled

High Quality Factor Photonic Crystal Micro-Cavity Design to Utilize Semiconductor Nonlinearities

2006 ◽  
Vol 45 (3A) ◽  
pp. 1612-1616 ◽  
Author(s):  
Achanta Venu Gopal ◽  
Akihisa Tomita ◽  
Sheng Lan ◽  
Hirohito Yamada ◽  
Jun Ushida
Keyword(s):  
Photonic Crystal ◽  
Quality Factor ◽  
High Quality Factor ◽  
High Quality ◽  
Cavity Design ◽  
Micro Cavity

A testing paradigm for quantifying ICC profilers

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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