A computational model of print-quality perception

A system has been developed that can simulate human print quality assessments for simple prints. It consists of an image analysis system and a neural network trained in differentiating between different quality prints. Humans were used to assess the print quality of a series of images of different tones, produced by a variety of printing processes. An image analysis system was employed to collect and pre-process raw image data from the prints. A neural network employing supervised learning was then used to produce computer models of the assessments. The image analysis system and neural network models were subsequently employed to predict the observer assessments for a further set of prints that had not undergone the supervised learning procedure. In the prediction trial, the system correctly classified 23 out of 24 prints. (C) 1999 Published by Elsevier Science Ltd. All rights reserved.