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Perceptual quality assessment of the videos acquired in the wilds is of vitalimportance for quality assurance of video services. The inaccessibility ofreference videos with pristine quality and the complexity of authenticdistortions pose great challenges for this kind of blind video qualityassessment (BVQA) task. Although model-based transfer learning is an effectiveand efficient paradigm for the BVQA task, it remains to be a challenge toexplore what and how to bridge the domain shifts for better videorepresentation. In this work, we propose to transfer knowledge from imagequality assessment (IQA) databases with authentic distortions and large-scaleaction recognition with rich motion patterns. We rely on both groups of data tolearn the feature extractor. We train the proposed model on the target VQAdatabases using a mixed list-wise ranking loss function. Extensive experimentson six databases demonstrate that our method performs very competitively underboth individual database and mixed database training settings. We also verifythe rationality of each component of the proposed method and explore a simplemanner for further improvement.

Blindly Assess Quality of In-the-Wild Videos via Quality-aware Pre-training and Motion Perception