Temporal attention networks for no-reference video quality assessment
Using attention to extend image quality assessment algorithms for the task of video quality assessment.
Two-stage learning
Attention is used over features extracted by SOTA no-reference IQA algorithms in attention.py.
End-to-end learning
Paq-2-Piq (an IQA model that uses deep-learning) is paired with an RNN and attention (CNN+RNN framework) to learn VQA scores end-to-end in vqa.py.
ann.py trains feature vectors that are averaged across frames. attention.py trains an attention network to weigh frame features before training. vqa.py performs end-to-end training with attention and Paq-2-Piq.
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