  Traditional video quality assessment (VQA) methods evaluate localized picture
quality and video score is predicted by temporally aggregating frame scores.
However, video quality exhibits different characteristics from static image
quality due to the existence of temporal masking effects. In this paper, we
present a novel architecture, namely C3DVQA, that uses Convolutional Neural
Network with 3D kernels (C3D) for full-reference VQA task. C3DVQA combines
feature learning and score pooling into one spatiotemporal feature learning
process. We use 2D convolutional layers to extract spatial features and 3D
convolutional layers to learn spatiotemporal features. We empirically found
that 3D convolutional layers are capable to capture temporal masking effects of
videos. We evaluated the proposed method on the LIVE and CSIQ datasets. The
experimental results demonstrate that the proposed method achieves the
state-of-the-art performance.
