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 and use a mixed list-wise ranking loss function to train the entire model on the target VQA databases. Extensive experiments on six benchmarking databases demonstrate that our method performs very competitively under both individual database and mixed databases training settings. We also verify the rationality of each component of the proposed method and explore a simple ensemble trick for further improvement.

中文翻译：

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通过质量感知预训练和运动感知盲目评估野外视频的质量

在野外获取的视频的感知质量评估对于视频服务的质量保证至关重要。具有原始质量的参考视频的不可访问性和真实失真的复杂性对这种盲视频质量评估 (BVQA) 任务构成了巨大挑战。尽管基于模型的迁移学习是 BVQA 任务的有效范例，但它仍然是一个探索的挑战什么和如何桥接域转换以获得更好的视频表示。在这项工作中，我们建议从具有真实失真和具有丰富运动模式的大规模动作识别的图像质量评估 (IQA) 数据库中转移知识。我们依靠两组数据来学习特征提取器，并使用混合列表排序损失函数在目标 VQA 数据库上训练整个模型。在六个基准数据库上进行的大量实验表明，我们的方法在单个数据库和混合数据库训练设置下都表现得非常有竞争力。我们还验证了所提出方法的每个组件的合理性，并探索了一个简单的集成技巧以进一步改进。