The Influence of Image Content and Paper Grade on Quality Attributes Computed from Printed Natural Images

Computation of quality from digital photographic images has been widely studied whereas research on quality computation from printed natural images has been scarce to date. This study was motivated by needs to develop characterization of the quality potential of paper for digital printing by electrophotography and ink-jet employing subjectively meaningful objective methods. The goal was to find whether commonly used algorithms of blur, noise, contrast and colorfulness are feasible for quality characterization within the range of variation originating from paper and to evaluate whether the performance of paper grades is dependent on image content type. According to the results, image content is highly important and the applicability of the algorithms is complicated by the role of noise in prints.