  Video quality assessment (VQA) is an important problem in computer vision.
The videos in computer vision applications are usually captured in the wild. We
focus on automatically assessing the quality of in-the-wild videos, which is a
challenging problem due to the absence of reference videos, the complexity of
distortions, and the diversity of video contents. Moreover, the video contents
and distortions among existing datasets are quite different, which leads to
poor performance of data-driven methods in the cross-dataset evaluation
setting. To improve the performance of quality assessment models, we borrow
intuitions from human perception, specifically, content dependency and
temporal-memory effects of human visual system. To face the cross-dataset
evaluation challenge, we explore a mixed datasets training strategy for
training a single VQA model with multiple datasets. The proposed unified
framework explicitly includes three stages: relative quality assessor,
nonlinear mapping, and dataset-specific perceptual scale alignment, to jointly
predict relative quality, perceptual quality, and subjective quality.
Experiments are conducted on four publicly available datasets for VQA in the
wild, i.e., LIVE-VQC, LIVE-Qualcomm, KoNViD-1k, and CVD2014. The experimental
results verify the effectiveness of the mixed datasets training strategy and
prove the superior performance of the unified model in comparison with the
state-of-the-art models. For reproducible research, we make the PyTorch
implementation of our method available at https://github.com/lidq92/MDTVSFA.
