Deep VQA based on a Novel Hybrid Training Methodology

In recent years, deep learning techniques have been widely applied to video quality assessment (VQA), showing significant potential to achieve higher correlation performance with subjective opinions compared to conventional approaches. However, these methods are often developed based on limited training materials and evaluated through cross validation, due to the lack of large scale subjective databases. In this context, this paper proposes a new hybrid training methodology, which generates large volumes of training data by using quality indices from an existing perceptual quality metric, VMAF, as training targets, instead of actual subjective opinion scores. An additional shallow CNN is also employed for temporal pooling, which was trained based on a small subjective video database. The resulting Deep Video Quality Metric (based on Hybrid Training), DVQM-HT, has been fully tested on eight HD subjective video databases, and consistently exhibits higher correlation with perceptual quality compared to other deep quality assessment methods, with an average SROCC value of 0.8263.