Predicting Encoded Picture Quality in Two Steps is a Better Way

Full-reference (FR) image quality assessment (IQA) models assume a high quality "pristine" image as a reference against which to measure perceptual image quality. In many applications, however, the assumption that the reference image is of high quality may be untrue, leading to incorrect perceptual quality predictions. To address this, we propose a new two-step image quality prediction approach which integrates both no-reference (NR) and full-reference perceptual quality measurements into the quality prediction process. The no-reference module accounts for the possibly imperfect quality of the source (reference) image, while the full-reference component measures the quality differences between the source image and its possibly further distorted version. A simple, yet very efficient, multiplication step fuses the two sources of information into a reliable objective prediction score. We evaluated our two-step approach on a recently designed subjective image database and achieved standout performance compared to full-reference approaches, especially when the reference images were of low quality. The proposed approach is made publicly available at https://github.com/xiangxuyu/2stepQA