Image Quality Assessments by Leveraging Diverse Visual Tasks

Image quality assessment (IQA) is a fundamental task in computer vision with the goal of accurately predicting the mean opinion score of humans for assessing the quality of images. While recent advances in deep neural networks (DNNs) have sparked much research on IQA, with the hope for IQA to mimic humans effectively, there has been a lack of systematic and analytical research on understanding what factors humans prioritize during IQA. This paper aims to identify human priorities in image evaluation by leveraging the DNN models for diverse computer vision tasks and proposes simple, but effective IQA metrics through our comprehensive analyses on those models. Then, these analyses led us to propose a novel vision-ensemble IQA (VE-IQA) method that demonstrated superior performance as compared to prior arts in IQA on popular IQA benchmarks such as LIVE, CSIQ, and TID2013.