  Objective image quality assessment deals with the prediction of digital
images' perceptual quality. No-reference image quality assessment predicts the
quality of a given input image without any knowledge or information about its
pristine (distortion free) counterpart. Machine learning algorithms are heavily
used in no-reference image quality assessment because it is very complicated to
model the human visual system's quality perception. Moreover, no-reference
image quality assessment algorithms are evaluated on publicly available
benchmark databases. These databases contain images with their corresponding
quality scores. In this study, we evaluate several machine learning based
NR-IQA methods and one opinion unaware method on databases consisting of
authentic distortions. Specifically, LIVE In the Wild and KonIQ-10k databases
were applied to evaluate the state-of-the-art. For machine learning based
methods, appx. 80% were used for training and the remaining 20% were used for
testing. Furthermore, average PLCC, SROCC, and KROCC values were reported over
100 random train-test splits. The statistics of PLCC, SROCC, and KROCC values
were also published using boxplots. Our evaluation results may be helpful to
obtain a clear understanding about the status of state-of-the-art no-reference
image quality assessment methods.
