  Understanding semantic information is an essential step in knowing what is
being learned in both full-reference (FR) and no-reference (NR) image quality
assessment (IQA) methods. However, especially for many severely distorted
images, even if there is an undistorted image as a reference (FR-IQA), it is
difficult to perceive the lost semantic and texture information of distorted
images directly. In this paper, we propose a Mask Reference IQA (MR-IQA) method
that masks specific patches of a distorted image and supplements missing
patches with the reference image patches. In this way, our model only needs to
input the reconstructed image for quality assessment. First, we design a mask
generator to select the best candidate patches from reference images and
supplement the lost semantic information in distorted images, thus providing
more reference for quality assessment; in addition, the different masked
patches imply different data augmentations, which favors model training and
reduces overfitting. Second, we provide a Mask Reference Network (MRNet): the
dedicated modules can prevent disturbances due to masked patches and help
eliminate the patch discontinuity in the reconstructed image. Our method
achieves state-of-the-art performances on the benchmark KADID-10k, LIVE and
CSIQ datasets and has better generalization performance across datasets. The
code and results are available in the supplementary material.
