A comprehensive evaluation of full-reference image quality assessment algorithms on KADID-10k
This work addresses the need for up-to-date evaluations in image quality assessment for researchers and practitioners, but it is incremental as it applies existing methods to new data.
The study tackled the problem of evaluating full-reference image quality assessment algorithms by conducting a comprehensive evaluation using the KADID-10k database, the largest available at the time, to provide insights into the status of state-of-the-art metrics.
Significant progress has been made in the past decade for full-reference image quality assessment (FR-IQA). However, new large scale image quality databases have been released for evaluating image quality assessment algorithms. In this study, our goal is to give a comprehensive evaluation of state-of-the-art FR-IQA metrics using the recently published KADID-10k database which is largest available one at the moment. Our evaluation results and the associated discussions is very helpful to obtain a clear understanding about the status of state-of-the-art FR-IQA metrics.