ChipQA: No-Reference Video Quality Prediction via Space-Time Chips
This work addresses video quality prediction for applications like streaming and broadcasting, offering a more efficient solution, though it is incremental as it builds on existing natural video statistics models.
The authors tackled the problem of no-reference video quality assessment by introducing Space-Time Chips, a method that uses localized space-time slices to capture motion implicitly, and showed that it achieves state-of-the-art performance on several large databases without requiring motion computation.
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.