  Development of perceptual image quality assessment (IQA) metrics has been of
significant interest to computer vision community. The aim of these metrics is
to model quality of an image as perceived by humans. Recent works in
Full-reference IQA research perform pixelwise comparison between deep features
corresponding to query and reference images for quality prediction. However,
pixelwise feature comparison may not be meaningful if distortion present in
query image is severe. In this context, we explore utilization of no-reference
features in Full-reference IQA task. Our model consists of both full-reference
and no-reference branches. Full-reference branches use both distorted and
reference images, whereas No-reference branch only uses distorted image. Our
experiments show that use of no-reference features boosts performance of image
quality assessment. Our model achieves higher SRCC and KRCC scores than a
number of state-of-the-art algorithms on KADID-10K and PIPAL datasets.
