Print mottle is one of the most significant defects in modern offset printing influencing overall print quality.
Mottling can be defined as undesired unevenness in perceived print density. Previous research in the field
considered designing and improving perception models for evaluating print mottle. Mottle has traditionally
been evaluated by estimating the reflectance variation in the print. In our work, we present an approach of
estimating mottling effect prior to printing. Our experiments included imaging non printed media under various
lighting conditions, printing the samples with sheet fed offset printing and imaging afterwards. For the preprint
examinations we used a set of preprint images and for the outcome testing we used high resolution scans. For
the set of papers used in experiment only uncoated mechanical speciality paper showed a good chance of print
mottle prediction. Other tested paper types had a low correlation between non-printed and printed images.
The achieved results allow predicting the amount of mottling on the final print using preprint area images for
a certain paper type. Current experiment settings suited well for uncoated paper, but for the coated samples
other settings need to be tested. The results show that the estimation can be made on the coarse scale and for
better results extra parameters will be required, i.e., paper type, coating, printing process in question.
There are several important standard laboratory experiments for determining the quality of produced paper in the paper making industry. To know the quality is essential since it defines the use of paper for various purposes. Moreover, customers are expecting a certain degree of quality. Many of paper printability tests are based on off-line visual inspection. Currently these tests are done by printing test marks on a piece of paper and then observing the quality by a human evaluator. In this report visual inspection on paper by machine vision is discussed from a point of off-line
industrial measurements. The work focuses on the following paper printability problems: missing dots (Heliotest), print dot density, unevenness of printing image, surface strength (IGT), ink setting, linting, fiber counting, and digital printing. Compared to visual inspection by human evaluation, automated machine vision systems could offer several useful advantages: less deviations in measurements, better measurement accuracy, new printability parameters, shorter measurement times, less manpower to monotonic measurements, many quality parameters by one system, and automatic
data transfer to mill level information systems. Current results with paper and board samples indicate that human evaluators could be replaced. However, further research is needed since the printability problems vary mill by mill, there is a large number of various paper and board samples, and the relationships between off-line and on-line measurements must be considered.