Comprehensive evaluation of no-reference image quality assessment algorithms on KADID-10k database
This work provides a benchmark for researchers and practitioners in computer vision to understand the current state-of-the-art in no-reference image quality assessment, though it is incremental as it evaluates existing methods on a new database.
The study conducted a comprehensive evaluation of no-reference image quality assessment algorithms using the KADID-10k database, reporting average PLCC, SROCC, and KROCC metrics over 100 random train-test splits to assess their performance against subjective evaluations.
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.