  In this work, we present a simple yet effective unified model for perceptual
quality assessment of image and video. In contrast to existing models which
usually consist of complex network architecture, or rely on the concatenation
of multiple branches of features, our model achieves a comparable performance
by applying only one global feature derived from a backbone network (i.e.
resnet18 in the presented work). Combined with some training tricks, the
proposed model surpasses the current baselines of SOTA models on public and
private datasets. Based on the architecture proposed, we release the models
well trained for three common real-world scenarios: UGC videos in the wild, PGC
videos with compression, Game videos with compression. These three pre-trained
models can be directly applied for quality assessment, or be further fine-tuned
for more customized usages. All the code, SDK, and the pre-trained weights of
the proposed models are publicly available at
https://github.com/Tencent/CenseoQoE.
