  Convolutional Neural Network (CNN)-based filters have achieved significant
performance in video artifacts reduction. However, the high complexity of
existing methods makes it difficult to be applied in real usage. In this paper,
a CNN-based low complexity filter is proposed. We utilize depth separable
convolution (DSC) merged with the batch normalization (BN) as the backbone of
our proposed CNN-based network. Besides, a weight initialization method is
proposed to enhance the training performance. To solve the well known over
smoothing problem for the inter frames, a frame-level residual mapping (RM) is
presented. We analyze some of the mainstream methods like frame-level and
block-level based filters quantitatively and build our CNN-based filter with
frame-level control to avoid the extra complexity and artificial boundaries
caused by block-level control. In addition, a novel module called RM is
designed to restore the distortion from the learned residuals. As a result, we
can effectively improve the generalization ability of the learning-based filter
and reach an adaptive filtering effect. Moreover, this module is flexible and
can be combined with other learning-based filters. The experimental results
show that our proposed method achieves significant BD-rate reduction than
H.265/HEVC. It achieves about 1.2% BD-rate reduction and 79.1% decrease in
FLOPs than VR-CNN. Finally, the measurement on H.266/VVC and ablation studies
are also conducted to ensure the effectiveness of the proposed method.
