We propose a new model for no-reference video quality assessment (VQA). Our approach uses a new idea of highly-localized space-time (ST) slices called Space-Time Chips (ST Chips). ST Chips are localized cuts of video data along directions that \textit{implicitly} capture motion. We use perceptually-motivated bandpass and normalization models to first process the video data, and then select oriented ST Chips based on how closely they fit parametric models of natural video statistics. We show that the parameters that describe these statistics can be used to reliably predict the quality of videos, without the need for a reference video. The proposed method implicitly models ST video naturalness, and deviations from naturalness. We train and test our model on several large VQA databases, and show that our model achieves state-of-the-art performance at reduced cost, without requiring motion computation.

本研究提出了一种新的无参考视频质量评估模型，使用高度本地化的时空切片来隐式地捕捉运动，并使用符合人类感知的带通滤波和规范化模型对视频数据进行处理，然后基于这些时空切片来预测视频的质量，无需参考视频，并在多个大型VQA数据库上进行了测试，取得了最先进的性能。

ChipQA: 通过时空芯片实现无参考视频质量预测