  The main goal of objective image quality assessment is to devise
computational, mathematical models which are able to predict perceptual image
quality consistently with subjective evaluations. The evaluation of objective
image quality assessment algorithms is based on experiments conducted on
publicly available benchmark databases. In this study, our goal is to give a
comprehensive evaluation about no-reference image quality assessment
algorithms, whose original source codes are available online, using the
recently published KADID-10k database which is one of the largest available
benchmark databases. Specifically, average PLCC, SROCC, and KROCC are reported
which were measured over 100 random train-test splits. Furthermore, the
database was divided into a train (appx. 80\% of images) and a test set (appx.
20% of images) with respect to the reference images. So no semantic content
overlap was between these two sets. 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.
