No-reference video quality metrics are becoming ever more popular, as they are more useful in real-life applications compared to full-reference metrics. One way to design such metrics is by applying data analysis methods on both objectively measurable features and data from subjective testing. Partial least squares regression (PLSR) is one such method. But in order to apply such methods, we have to temporally pool over all frames of a video, loosing valuable information about the quality variation over time. Hence, we extend the PLSR into a higher dimensional space with multiway PLSR in this contribution and thus consider video in all its dimensions. We designed a H.264/AVC bitstream no-reference video quality metric in order to verify multiway PLSR against PLSR with respect to the prediction performance. Our results show that the inclusion of the temporal dimension with multiway PLSR improves the quality prediction and its correlation with the actual quality.     «

No-reference video quality metrics are becoming ever more popular, as they are more useful in real-life applications compared to full-reference metrics. One way to design such metrics is by applying data analysis methods on both objectively measurable features and data from subjective testing. Partial least squares regression (PLSR) is one such method. But in order to apply such methods, we have to temporally pool over all frames of a video, loosing valuable information about the quality variati...     »