soft proofing
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2021 ◽  
Vol 2021 (29) ◽  
pp. 241-246
Author(s):  
Gregory High

This paper investigates the impact of the Helmholtz-Kohlrausch effect on near-white substrate colours. As the luminance of the test colour (or its simulated reflectance in a softproof setup) approaches that of the adapting white point the viewing mode changes from 'surface mode' to 'aperture mode', and the appearance of the test colour becomes self-luminous. However, some substrates with optical brighteners fall close to this threshold between viewing modes, since the OBAs not only increase the perceived reflectance but also increase the H-K effect, where it is very prominent in bluish colours. For graphic arts content shown on a display system, this essentially breaks the soft-proofing paradigm. The practical application of this work relates to cross-media colour reproduction, where the lightness appearance of some substrates is not adequately described by their colorimetric values, and this may impact on choice of proofing strategies.


2021 ◽  
Author(s):  
Nawar Fdhal

In this thesis, an adaptive mechanism for controlling the illumination is combined with a closed loop technique and the use of High Dynamic range (HDR) to generate a black box model that can simulate the hard proof of a given digital image. An adaptive Artificial Neural Network (ANN) was used to create the black box model, using the camera as a measuring device. The non-uniformity of the illumination in the viewing booth is typically a barrier in creating such a black box model since color appearance varies with location in the viewing booth. This issue was addressed in this thesis by compensation for viewing booth illumination using an inexpensive camera and a Liquid Crystal Display (LCD) projector. HDR was found to give a favourable representation that is more indicative of the image perceived by the operator, and was used as the basis for mapping the original image to the soft proof. A proof of concept was also developed to highlight the utility of the LCD projector based approach in providing a more broad range of varying intensity color illuminants (thus environments) under which a proof may be not only viewed, but modeled through the closed loop process. In this sense, a system has been developed to generate and provide custom soft proofs that can extend the functionality of the standard viewing booth. The proposed technique will open the doors to new automated systems that can be very beneficial to the printing industry.


2021 ◽  
Author(s):  
Nawar Fdhal

In this thesis, an adaptive mechanism for controlling the illumination is combined with a closed loop technique and the use of High Dynamic range (HDR) to generate a black box model that can simulate the hard proof of a given digital image. An adaptive Artificial Neural Network (ANN) was used to create the black box model, using the camera as a measuring device. The non-uniformity of the illumination in the viewing booth is typically a barrier in creating such a black box model since color appearance varies with location in the viewing booth. This issue was addressed in this thesis by compensation for viewing booth illumination using an inexpensive camera and a Liquid Crystal Display (LCD) projector. HDR was found to give a favourable representation that is more indicative of the image perceived by the operator, and was used as the basis for mapping the original image to the soft proof. A proof of concept was also developed to highlight the utility of the LCD projector based approach in providing a more broad range of varying intensity color illuminants (thus environments) under which a proof may be not only viewed, but modeled through the closed loop process. In this sense, a system has been developed to generate and provide custom soft proofs that can extend the functionality of the standard viewing booth. The proposed technique will open the doors to new automated systems that can be very beneficial to the printing industry.


2019 ◽  
Vol 2019 (6) ◽  
pp. 479-1-479-6
Author(s):  
Ingeborg Tastl ◽  
Miguel A Lopez-Alvarez ◽  
Alex Ju ◽  
Morgan Schramm ◽  
Jordi Roca ◽  
...  
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2018 ◽  
Vol 33 (4) ◽  
pp. 285-290
Author(s):  
万 勇 WAN Yong ◽  
郑元林 ZHENG Yuan-lin ◽  
游伴奏 YOU Ban-zou
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