  Full-reference (FR) image quality assessment (IQA) evaluates the visual
quality of a distorted image by measuring its perceptual difference with
pristine-quality reference, and has been widely used in low-level vision tasks.
Pairwise labeled data with mean opinion score (MOS) are required in training
FR-IQA model, but is time-consuming and cumbersome to collect. In contrast,
unlabeled data can be easily collected from an image degradation or restoration
process, making it encouraging to exploit unlabeled training data to boost
FR-IQA performance. Moreover, due to the distribution inconsistency between
labeled and unlabeled data, outliers may occur in unlabeled data, further
increasing the training difficulty. In this paper, we suggest to incorporate
semi-supervised and positive-unlabeled (PU) learning for exploiting unlabeled
data while mitigating the adverse effect of outliers. Particularly, by treating
all labeled data as positive samples, PU learning is leveraged to identify
negative samples (i.e., outliers) from unlabeled data. Semi-supervised learning
(SSL) is further deployed to exploit positive unlabeled data by dynamically
generating pseudo-MOS. We adopt a dual-branch network including reference and
distortion branches. Furthermore, spatial attention is introduced in the
reference branch to concentrate more on the informative regions, and sliced
Wasserstein distance is used for robust difference map computation to address
the misalignment issues caused by images recovered by GAN models. Extensive
experiments show that our method performs favorably against state-of-the-arts
on the benchmark datasets PIPAL, KADID-10k, TID2013, LIVE and CSIQ.
