A Comparative Evaluation of Temporal Pooling Methods for Blind Video Quality Assessment
This work addresses the selection of temporal pooling strategies for researchers and practitioners in video quality assessment, but it is incremental as it builds on existing methods without introducing a new paradigm.
The study compared various temporal pooling methods for blind video quality assessment on user-generated videos, finding that an ensemble pooling model outperformed individual methods, with specific performance gains reported across two large-scale databases.
Many objective video quality assessment (VQA) algorithms include a key step of temporal pooling of frame-level quality scores. However, less attention has been paid to studying the relative efficiencies of different pooling methods on no-reference (blind) VQA. Here we conduct a large-scale comparative evaluation to assess the capabilities and limitations of multiple temporal pooling strategies on blind VQA of user-generated videos. The study yields insights and general guidance regarding the application and selection of temporal pooling models. In addition, we also propose an ensemble pooling model built on top of high-performing temporal pooling models. Our experimental results demonstrate the relative efficacies of the evaluated temporal pooling models, using several popular VQA algorithms, and evaluated on two recent large-scale natural video quality databases. In addition to the new ensemble model, we provide a general recipe for applying temporal pooling of frame-based quality predictions.