  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.
