Relating halftone dot quality to paper surface topography

Most printed material is produced by printing halftone dot patterns. One of the key issues that determine the attainable print quality is the structure of the paper surface, but the relation is non-deterministic in nature. We examine the halftone print quality and study the statistical dependence be...

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Bibliographic Details
Published in:Neural computing & applications 2011-09, Vol.20 (6), p.803-813
Main Authors: Kumpulainen, Pekka, Mettänen, Marja, Lauri, Mikko, Ihalainen, Heimo
Format: Article
Language:English
Subjects:
Applied sciences
Artificial Intelligence
Computational Biology/Bioinformatics
Computational Science and Engineering
Computer Science
Computer science
control theory
systems
Data Mining and Knowledge Discovery
Eann 2009
Exact sciences and technology
Image Processing and Computer Vision
Learning and adaptive systems
Mathematics
Multivariate analysis
Probability and statistics
Probability and Statistics in Computer Science
Sciences and techniques of general use
Statistics
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Summary:Most printed material is produced by printing halftone dot patterns. One of the key issues that determine the attainable print quality is the structure of the paper surface, but the relation is non-deterministic in nature. We examine the halftone print quality and study the statistical dependence between the defects in printed dots and the topography measurement of the unprinted paper. The work concerns SC paper samples printed by an IGT gravure test printer. We have small-scale 2D measurements of the unprinted paper surface topography and the reflectance of the print result. The measurements before and after printing are aligned with subpixel resolution, and individual printed dots are detected. First, the quality of the printed dots is studied using Self Organizing Map and clustering and the properties of the corresponding areas in the unprinted topography are examined. The printed dots are divided into high and low print quality. Features from the unprinted paper surface topography are then used to classify the corresponding paper areas using Support Vector Machine classification. The results show that the topography of the paper can explain some of the print defects. However, there are many other factors that affect the print quality, and the topography alone is not adequate to predict the print quality.
ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-010-0497-y