  Convolutional neural networks have achieved excellent results in compressed
video quality enhancement task in recent years. State-of-the-art methods
explore the spatiotemporal information of adjacent frames mainly by deformable
convolution. However, offset fields in deformable convolution are difficult to
train, and its instability in training often leads to offset overflow, which
reduce the efficiency of correlation modeling. In this work, we propose a
transformer-based compressed video quality enhancement (TVQE) method,
consisting of Swin-AutoEncoder based Spatio-Temporal feature Fusion (SSTF)
module and Channel-wise Attention based Quality Enhancement (CAQE) module. The
proposed SSTF module learns both local and global features with the help of
Swin-AutoEncoder, which improves the ability of correlation modeling.
Meanwhile, the window mechanism-based Swin Transformer and the encoderdecoder
structure greatly improve the execution efficiency. On the other hand, the
proposed CAQE module calculates the channel attention, which aggregates the
temporal information between channels in the feature map, and finally achieves
the efficient fusion of inter-frame information. Extensive experimental results
on the JCT-VT test sequences show that the proposed method achieves better
performance in average for both subjective and objective quality. Meanwhile,
our proposed method outperforms existing ones in terms of both inference speed
and GPU consumption.
