Extending video quality metrics to the temporal dimension with 2D-PCR

The aim of any video quality metric is to deliver a quality prediction similar to the video quality perceived by human observers. One way to design such a model of human perception is by data analysis. In this contribution we intend to extend this approach to the temporal dimension. Even though video obviously consists of spatial and temporal dimensions, the temporal aspect is often not considered well enough. Instead of including this third dimension in the model itself, the metrics are usually only applied on a frame-by-frame basis and then temporally pooled, commonly by averaging. Therefore we propose to skip the temporal pooling step and use the additional temporal dimension in the model building step of the video quality metric. We propose to use the two dimensional extension of the PCR, the 2D-PCR, in order to obtain an improved model. We conducted extensive subjective tests with different HDTV video sequences at 1920×1080 and 25 frames per seconds. For verification, we performed a cross validation to get a measure for the real-life performance of the acquired model. Finally, we will show that the direct inclusion of the temporal dimension of video into the model building improves the overall prediction accuracy of the visual quality significantly.     «

The aim of any video quality metric is to deliver a quality prediction similar to the video quality perceived by human observers. One way to design such a model of human perception is by data analysis. In this contribution we intend to extend this approach to the temporal dimension. Even though video obviously consists of spatial and temporal dimensions, the temporal aspect is often not considered well enough. Instead of including this third dimension in the model itself, the metrics are usually...     »