With the development of image-based applications, assessing the quality of images has become increasingly important. Although our perception of image quality changes as we age, most existing image quality assessment (IQA) metrics make simplifying assumptions about the age of observers, thus limiting their use for age-specific applications. In this work, we propose a personalized IQA metric to assess the perceived image quality of observers from different age groups. Firstly, we apply an age simulation algorithm to compute how an observer with a particular age would perceive a given image. More specifically, we process the input image according to an age-specific contrast sensitivity function (CSF), which predicts the reduction of contrast visibility associated with the aging eye. We combine age simulation with existing IQA metrics to calculate the age-specific perceived image quality score. To validate the effectiveness of our combined model, we conducted a psychophysical experiment in a controlled laboratory environment with young (18-31 y.o.), middle-aged (32-52 y.o.), and older (53+ y.o.) adults, measuring their image quality preferences for 84 test images. Our analysis shows that the predictions by our age-specific IQA metric are well correlated with the collected subjective IQA results from our psychophysical experiment.
Macro-uniformity is an important factor in the overall quality of prints from inkjet printers. The International Committee for Information Technology Standards (INCITS) defined the macrouniformity for prints, which includes several printing defects such as banding, streaks, mottle, etc. Although we can quantitatively analyze a certain kind of defect, it is difficult to assess the overall perceptual quality when multiple defects appear simultaneously in a print. We used the Macro-uniformity quality rulers designed by INCITS W1.1 as experimental references, to conduct a psychophysical experiment for pooling perceptual assessments of our print samples from subjects. Then, calculated features can describe the severity of defects in a test sample; and we trained a predictive model using these data. The predictor can automatically predict the macro-uniformity score as judged by humans. Our results show that the predictor can work accurately. The predicted scores are similar to the subjective visual scores (ground-truth). Also, we used 6-fold cross-validation to confirm the efficacy of our predictor.