  In recent years, deep learning techniques have shown significant potential
for improving video quality assessment (VQA), achieving higher correlation with
subjective opinions compared to conventional approaches. However, the
development of deep VQA methods has been constrained by the limited
availability of large-scale training databases and ineffective training
methodologies. As a result, it is difficult for deep VQA approaches to achieve
consistently superior performance and model generalization. In this context,
this paper proposes new VQA methods based on a two-stage training methodology
which motivates us to develop a large-scale VQA training database without
employing human subjects to provide ground truth labels. This method was used
to train a new transformer-based network architecture, exploiting quality
ranking of different distorted sequences rather than minimizing the difference
from the ground-truth quality labels. The resulting deep VQA methods (for both
full reference and no reference scenarios), FR- and NR-RankDVQA, exhibit
consistently higher correlation with perceptual quality compared to the
state-of-the-art conventional and deep VQA methods, with average SROCC values
of 0.8972 (FR) and 0.7791 (NR) over eight test sets without performing
cross-validation. The source code of the proposed quality metrics and the large
training database are available at https://chenfeng-bristol.github.io/RankDVQA.
