Practical Video Quality Assessment Of User Generated Content

During the past few years, video quality assessment (VQA) of user generated content (UGC) has attracted considerable attention in the research community. In this paper, we propose a practical architecture for a versatile video quality model, designed for assessing user generated videos in particular. The proposed architecture is based on our earlier design of two-level video quality model with a convolutional neural network (CNN-TLVQM), with various improvements and re-designed elements. We have built a fast implementation of the proposed model in C++, demonstrating that the model is practical for real-life applications. The implementation of the model has been submitted for evaluation in ICME UGCVQA Challenge in 2021.