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In the context of print quality and process control colorimetric parameters and tolerance values are clearly defined.
Calibration procedures are well defined for color measurement instruments in printing workflows. Still, using more than
one color measurement instrument measuring the same color wedge can produce clearly different results due to random
and systematic errors of the instruments. In certain situations where one instrument gives values which are just inside the
given tolerances and another measurement instrument produces values which exceed the predefined tolerance
parameters, the question arises whether the print or proof is approved or not accepted with regards to the standard
parameters. The aim of this paper was to determine an appropriate model to characterize color measurement instruments
for printing applications in order to improve the colorimetric performance and hence the inter-instrument agreement. The
method proposed is derived from color image acquisition device characterization methods which have been applied by
performing polynomial regression with a least square technique. Six commercial color measurement instruments were
used for measuring color patches of a control color wedge on three different types of paper substrates. The
characterization functions were derived using least square polynomial regression, based on the training set of 14 BCRA
tiles colorimetric reference values and the corresponding colorimetric measurements obtained by the measurement
instruments. The derived functions were then used to correct the colorimetric values of test sets of 46 measurements of
the color control wedge patches. The corrected measurement results obtained from the applied regression model was
then used as the starting point with which the corrected measurements from other instruments were compared to find the
most appropriate polynomial, which results in the least color difference. The obtained results demonstrate that the
proposed regression method works remarkably well with a range of different color measurement instruments used on
three types of substrates. Finally, by extending the training set from 14 samples to 38 samples the obtained results clearly
indicate that the model is robust.
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