The Yule-Nielsen modified spectral Neugebauer model (YNSN) enables predicting reflectance spectra from ink surface coverages of halftones. To provide an improved prediction accuracy, this model is enhanced with an ink spreading model accounting for ink spreading in all superposition conditions (ISYNSN). As any other spectral reflection prediction model, the ISYNSN model is conceived to predict the reflection spectra of color-constant patches. Instead of color-constant patches, we investigate if tiles located within color images can be accurately predicted and how they can be used to facilitate the calibration of the ink spreading model. We detail an algorithm to automatically select image tiles as uniform as possible from color images by relying on their CMY or CMYK pixel values. The tile selection algorithm incorporates additional constraints relying on surface coverages of the inks. We demonstrate that an ink spreading model calibrated with as few as 5 to 10 optimally chosen image tiles allows the corresponding YNSN model to provide accurate spectral predictions.
The Yule-Nielsen modified spectral Neugebauer model (YNSN) enables predicting reflectance spectra from ink
surface coverages of halftones. In order to provide an improved prediction accuracy, this model is enhanced with
an ink spreading model accounting for ink spreading in all superposition conditions (IS-YNSN). As any other
spectral reflection prediction model, the IS-YNSN model is conceived to predict the reflection spectra of uniform
patches. Instead of uniform patches, we investigate if tiles located within color images can be accurately predicted
and how they can be used to facilitate the calibration of the ink spreading model. In the present contribution, we
first detail an algorithm to automatically select image tiles as uniform as possible from color images by relying on
the CMY or CMYK pixel values of these color tiles. We show that if these image tiles are uniform enough, they
can be accurately predicted by the IS-YNSN model. The selection algorithm incorporates additional constraints
and is verified on 6 different color images. We finally demonstrate that the ink spreading model can be calibrated
with as few as 5 to 10 image tiles.
The Yule-Nielsen modified spectral Neugebauer model enables predicting reflectance spectra from surface coverages. In
order to provide high prediction accuracy, this model is enhanced with an ink spreading model accounting for physical
dot gain. Traditionally, physical dot gain, also called mechanical dot gain, is modeled by one ink spreading curve per ink.
An ink spreading curve represents the mapping between nominal to effective dot surface coverages when an ink halftone
wedge is printed. In previous publications, we have shown that using one ink spreading curve per ink is not sufficient to
accurately model physical dot gain, and that the physical dot gain of a specific ink is modified by the presence of other
inks. We therefore proposed an ink spreading model taking all the ink superposition conditions into account. We now
show that not all superposition conditions are useful and necessary when working with cyan, magenta, yellow, and black
inks. We therefore study the influence of ink spreading in different superposition conditions on the accuracy of the
spectral prediction model. Finally,
The Yule-Nielsen modified Spectral Neugebauer reflection prediction model enhanced with an ink spreading model
provides high accuracy when predicting reflectance spectra from ink surface coverages. In the present contribution, we
try to inverse the model, i.e. to deduce the surface coverages of a printed color halftone patch from its measured
reflectance spectrum. This process yields good results for cyan, magenta, and yellow inks, but unstable results when
simultaneously fitting cyan, magenta, yellow, and black inks due to redundancy between these four inks: black can be
obtained by printing either the black ink or similar amounts of the cyan, magenta, and yellow inks. To overcome this
problem, we use the fact that the black pigmented ink absorbs light in the infrared domain, whereas cyan, magenta, and
yellow inks do not. Therefore, with reflection spectra measurements spanning both the visible and infrared domain, it is
possible to accurately deduce the black ink coverage. Since there is no redundancy anymore, the cyan, magenta, yellow,
and pigmented black ink coverages can be recovered with high accuracy.
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