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 by 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