  Quality assessment of in-the-wild videos is a challenging problem because of
the absence of reference videos and shooting distortions. Knowledge of the
human visual system can help establish methods for objective quality assessment
of in-the-wild videos. In this work, we show two eminent effects of the human
visual system, namely, content-dependency and temporal-memory effects, could be
used for this purpose. We propose an objective no-reference video quality
assessment method by integrating both effects into a deep neural network. For
content-dependency, we extract features from a pre-trained image classification
neural network for its inherent content-aware property. For temporal-memory
effects, long-term dependencies, especially the temporal hysteresis, are
integrated into the network with a gated recurrent unit and a
subjectively-inspired temporal pooling layer. To validate the performance of
our method, experiments are conducted on three publicly available in-the-wild
video quality assessment databases: KoNViD-1k, CVD2014, and LIVE-Qualcomm,
respectively. Experimental results demonstrate that our proposed method
outperforms five state-of-the-art methods by a large margin, specifically,
12.39%, 15.71%, 15.45%, and 18.09% overall performance improvements over the
second-best method VBLIINDS, in terms of SROCC, KROCC, PLCC and RMSE,
respectively. Moreover, the ablation study verifies the crucial role of both
the content-aware features and the modeling of temporal-memory effects. The
PyTorch implementation of our method is released at
https://github.com/lidq92/VSFA.
