Abstract
Recently, some transfer learning-based methods have been adopted
in video quality assessment (VQA) to compensate for the lack of
enormous training samples and human annotation labels. But these
methods induce a domain gap between source and target domains,
resulting in a sub-optimal feature representation that deteriorates the
accuracy. This paper proposes the optimized quality feature learning via a multi-channel convolutional neural network (CNN) with
the gated recurrent unit (GRU) for no-reference (NR) VQA. First,
the multi-channel CNN is pre-trained on the image quality assessment (IQA) domain using non-human annotation labels, which is
inspired by self-supervised learning. Then, semi-supervised learning is used to fine-tune CNN and transfer the knowledge from IQA
to VQA while considering motion information for optimized quality feature learning. Finally, all frame quality features are extracted
as the input of GRU to obtain the final video quality. Experimental
results demonstrate that our model achieves better performance than
state-of-the-art VQA approaches.
in video quality assessment (VQA) to compensate for the lack of
enormous training samples and human annotation labels. But these
methods induce a domain gap between source and target domains,
resulting in a sub-optimal feature representation that deteriorates the
accuracy. This paper proposes the optimized quality feature learning via a multi-channel convolutional neural network (CNN) with
the gated recurrent unit (GRU) for no-reference (NR) VQA. First,
the multi-channel CNN is pre-trained on the image quality assessment (IQA) domain using non-human annotation labels, which is
inspired by self-supervised learning. Then, semi-supervised learning is used to fine-tune CNN and transfer the knowledge from IQA
to VQA while considering motion information for optimized quality feature learning. Finally, all frame quality features are extracted
as the input of GRU to obtain the final video quality. Experimental
results demonstrate that our model achieves better performance than
state-of-the-art VQA approaches.
| Original language | English |
|---|---|
| Title of host publication | IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP’2023) |
| Publisher | IEEE |
| Pages | 1-5 |
| Publication status | Published - Jun 2023 |