Bayesian network model of overall print quality: Construction and structural optimisation

Authors:
Tuomas Eerola;Lasse Lensu;Joni-Kristian Kamarainen;Tuomas Leisti;Risto Ritala;Göte Nyman;Heikki Kälviäinen
Affiliations:
Machine Vision and Pattern Recognition Laboratory (MVPR), Lappeenranta University of Technology, P.O. Box 20, FIN-53851 Lappeenranta, Finland;Machine Vision and Pattern Recognition Laboratory (MVPR), Lappeenranta University of Technology, P.O. Box 20, FIN-53851 Lappeenranta, Finland;MVPR/Computational Vision Group (Kouvola Unit), Lappeenranta University of Technology, P.O. Box 20, FIN-53851 Lappeenranta, Finland;Department of Psychology, University of Helsinki, P.O. Box 9, FIN-00014 Helsinki, Finland;Department of Automation Science and Engineering, Tampere University of Technology, P.O. Box 692, FIN-33101 Tampere, Finland;Department of Psychology, University of Helsinki, P.O. Box 9, FIN-00014 Helsinki, Finland;Machine Vision and Pattern Recognition Laboratory (MVPR), Lappeenranta University of Technology, P.O. Box 20, FIN-53851 Lappeenranta, Finland
Venue:
Pattern Recognition Letters
Year:
2011

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Abstract

Prediction of overall visual quality based on instrumental measurements is a challenging task. Despite the several proposed models and methods, there exists a gap between the instrumental measurements of print and human visual assessment of natural images. In this work, a computational model for representing and quantifying the overall visual quality of prints is proposed. The computed overall quality should correspond to the human visual quality perception when viewing the printed images. The proposed model is a Bayesian network which connects the objective instrumental measurements to the subjective opinion distribution of human observers. This relationship can be used to score printed images, and additionally, to computationally study the connections of the attributes. A novel graphical learning approach using an iterative evolve-estimate-simulate loop learning the quality model based on psychometric data and instrumental measurements is suggested. The network structure is optimised by applying evolutionary computation (evolve). The estimation of the Bayesian network parameters is within the evolutionary loop. In this loop, the maximum likelihood approach is used (estimate). The stochastic learning process is guided by priors devised from the psychometric subjective experiments (performance through simulation). The model reveals and represents the explanatory factors between its elements providing insight to the psychophysical phenomenon of how observers perceive visual quality and which measurable entities affect the quality perception. By using true data, the design choices are demonstrated. It is also shown that the best-performing network establishes a clear and intuitively correct structure between the objective measurements and psychometric data.