  The explosion of user-generated videos stimulates a great demand for
no-reference video quality assessment (NR-VQA). Inspired by our observation on
the actions of human annotation, we put forward a Divide and Conquer Video
Quality Estimator (DCVQE) for NR-VQA. Starting from extracting the frame-level
quality embeddings (QE), our proposal splits the whole sequence into a number
of clips and applies Transformers to learn the clip-level QE and update the
frame-level QE simultaneously; another Transformer is introduced to combine the
clip-level QE to generate the video-level QE. We call this hierarchical
combination of Transformers as a Divide and Conquer Transformer (DCTr) layer.
An accurate video quality feature extraction can be achieved by repeating the
process of this DCTr layer several times. Taking the order relationship among
the annotated data into account, we also propose a novel correlation loss term
for model training. Experiments on various datasets confirm the effectiveness
and robustness of our DCVQE model.
