  Owing to the proliferation of user-generated videos on the Internet, blind
video quality assessment (BVQA) at the edge attracts growing attention. The
usage of deep-learning-based methods is restricted to be applied at the edge
due to their large model sizes and high computational complexity. In light of
this, a novel lightweight BVQA method called GreenBVQA is proposed in this
work. GreenBVQA features a small model size, low computational complexity, and
high performance. Its processing pipeline includes: video data cropping,
unsupervised representation generation, supervised feature selection, and
mean-opinion-score (MOS) regression and ensembles. We conduct experimental
evaluations on three BVQA datasets and show that GreenBVQA can offer
state-of-the-art performance in PLCC and SROCC metrics while demanding
significantly smaller model sizes and lower computational complexity. Thus,
GreenBVQA is well-suited for edge devices.
