Visual Print Quality Evaluation Using Computational Features

The ultimate print quality evaluation is always based on endusers' "quality experience", and therefore, the main challenge in automatic evaluation is to model the visual path and cognition process from physical properties to the experience. The present efforts to automate print quality evaluation have been concentrated on automating the current manually-performed assesments, which reduces the laborious work, but does not provide any novel information. In this work, a new approach for automating the evaluation is proposed and the approach is utilised by defining new computational level features which are able to explain visual quality evaluations performed by human experts.