  With recent advances in deep learning, numerous algorithms have been
developed to enhance video quality, reduce visual artefacts and improve
perceptual quality. However, little research has been reported on the quality
assessment of enhanced content - the evaluation of enhancement methods is often
based on quality metrics that were designed for compression applications. In
this paper, we propose a novel blind deep video quality assessment (VQA) method
specifically for enhanced video content. It employs a new Recurrent Memory
Transformer (RMT) based network architecture to obtain video quality
representations, which is optimised through a novel content-quality-aware
contrastive learning strategy based on a new database containing 13K training
patches with enhanced content. The extracted quality representations are then
combined through linear regression to generate video-level quality indices. The
proposed method, RMT-BVQA, has been evaluated on the VDPVE (VQA Dataset for
Perceptual Video Enhancement) database through a five-fold cross validation.
The results show its superior correlation performance when compared to ten
existing no-reference quality metrics.
