Video quality assessment (VQA) has sparked a lot of interest in the computer vision community, as it plays a critical role in services that provide customers with high quality video content. Due to the lack of high quality reference videos and the difficulties in collecting subjective evaluations, assessing video quality is a challenging and still unsolved problem. Moreover, most of the public research efforts focus only on user-generated content (UGC), making it unclear if reliable solutions can be adopted for assessing the quality of production-related videos. The goal of this work is to assess the importance of spatial and temporal learning for production-related VQA. In particular, it assesses state-of-the-art UGC video quality assessment perspectives on LIVE-APV dataset, demonstrating the importance of learning contextual characteristics from each video frame, as well as capturing temporal correlations between them.
For the full paper, see On the importance of spatio-temporal learning for video quality assessment on the Amazon Science website.