Measuring the visual quality of printed media is important since printed products have an important role in everyday life. Finding ways to automatically predict the image quality has been an active research topic in digital image processing, but adapting those methods to measure the visual quality of printed media has not been studied often or in depth and is not straightforward. Here, we analyze the efficacy of no-reference image quality assessment (IQA) algorithms originally developed for digital IQA with regards to predicting the perceived quality of printed natural images. We perform a comprehensive statistical comparison of the methods. The best methods are shown to accurately predict subjective opinions of the quality of printed photographs using data from a psychometric study.
Due to the rise in performance of digital printing, image-based applications are gaining popularity. This creates needs for
specifying the quality potential of printers and materials in more detail than before. Both production and end-use
standpoints are relevant. This paper gives an overview of an
on-going study which has the goal of determining a
framework model for the visual quality potential of paper in color image printing. The approach is top-down and it is
founded on the concept of a layered network model. The model and its subjective, objective and instrumental
measurement layers are discussed. Some preliminary findings are presented. These are based on data from samples
obtained by printing natural image contents and simple test fields on a wide range of paper grades by ink-jet in a color
managed process. Color profiles were paper specific. Visual mean opinion score data by human observers could be
accounted for by two or three dimensions. In the first place these are related to brightness and color brightness. Image
content has a marked effect on the dimensions. This underlines the challenges in designing the test images.