  Perceptual quality assessment of the videos acquired in the wilds is of vital
importance for quality assurance of video services. The inaccessibility of
reference videos with pristine quality and the complexity of authentic
distortions pose great challenges for this kind of blind video quality
assessment (BVQA) task. Although model-based transfer learning is an effective
and efficient paradigm for the BVQA task, it remains to be a challenge to
explore what and how to bridge the domain shifts for better video
representation. In this work, we propose to transfer knowledge from image
quality assessment (IQA) databases with authentic distortions and large-scale
action recognition with rich motion patterns. We rely on both groups of data to
learn the feature extractor. We train the proposed model on the target VQA
databases using a mixed list-wise ranking loss function. Extensive experiments
on six databases demonstrate that our method performs very competitively under
both individual database and mixed database training settings. We also verify
the rationality of each component of the proposed method and explore a simple
manner for further improvement.
