Printing with white ink plays an important role in many printing processes, but white is difficult to integrate into colour
management processes since conventional measurements are uncorrelated with the ink amount.
A control method for white ink is proposed in which white is printed and measured over black. The resulting
colorimetric densities can be modelled by polynomial regression, allowing accurate prediction of tonal value. The model
can readily be inverted to predict the colorant amount required to match a given colorimetric density, and hence is a
suitable method of measurement that can support process control and colour management.
KEYWORDS: Visualization, Visual process modeling, Signal to noise ratio, Contrast sensitivity, Interference (communication), Visual system, Signal processing, RGB color model, Eye, Image processing
Since the signal to noise measuring method as standardized in the normative part of ISO 15739:2002(E)1 does not
quantify noise in a way that matches the perception of the human eye, two alternative methods have been investigated
which may be appropriate to quantify the noise perception in a physiological manner: - the model of visual noise measurement proposed by Hung et al2 (as described in the informative annex of ISO
15739:20021) which tries to simulate the process of human vision by using the opponent space and contrast sensitivity
functions and uses the CIEL*u*v*1976 colour space for the determination of a so called visual noise value. - The S-CIELab model and CIEDE2000 colour difference proposed by Fairchild et al3 which simulates human vision
approximately the same way as Hung et al2 but uses an image comparison afterwards based on CIEDE2000.
With a psychophysical experiment based on just noticeable difference (JND), threshold images could be defined, with
which the two approaches mentioned above were tested. The assumption is that if the method is valid, the different
threshold images should get the same 'noise value'.
The visual noise measurement model results in similar visual noise values for all the threshold images. The method is
reliable to quantify at least the JND for noise in uniform areas of digital images. While the visual noise measurement
model can only evaluate uniform colour patches in images, the S-CIELab model can be used on images with spatial
content as well. The S-CIELab model also results in similar colour difference values for the set of threshold images, but
with some limitations: for images which contain spatial structures besides the noise, the colour difference varies
depending on the contrast of the spatial content.
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