  Video quality is a primary concern for video service providers. In recent
years, the techniques of video quality assessment (VQA) based on deep
convolutional neural networks (CNNs) have been developed rapidly. Although
existing works attempt to introduce the knowledge of the human visual system
(HVS) into VQA, there still exhibit limitations that prevent the full
exploitation of HVS, including an incomplete model by few characteristics and
insufficient connections among these characteristics. To overcome these
limitations, this paper revisits HVS with five representative characteristics,
and further reorganizes their connections. Based on the revisited HVS, a
no-reference VQA framework called HVS-5M (NRVQA framework with five modules
simulating HVS with five characteristics) is proposed. It works in a
domain-fusion design paradigm with advanced network structures. On the side of
the spatial domain, the visual saliency module applies SAMNet to obtain a
saliency map. And then, the content-dependency and the edge masking modules
respectively utilize ConvNeXt to extract the spatial features, which have been
attentively weighted by the saliency map for the purpose of highlighting those
regions that human beings may be interested in. On the other side of the
temporal domain, to supplement the static spatial features, the motion
perception module utilizes SlowFast to obtain the dynamic temporal features.
Besides, the temporal hysteresis module applies TempHyst to simulate the memory
mechanism of human beings, and comprehensively evaluates the quality score
according to the fusion features from the spatial and temporal domains.
Extensive experiments show that our HVS-5M outperforms the state-of-the-art VQA
methods. Ablation studies are further conducted to verify the effectiveness of
each module towards the proposed framework.
