An Efficient Low-Complexity Convolutional Neural Network Filter

Chao Liu, Heming Sun, Jiro Katto, Xiaoyang Zeng, Yibo Fan

Research output: Contribution to journalArticlepeer-review


Convolutional neural network (CNN) filters have achieved significant performance in video artifacts reduction. However, the high complexity of existing methods makes them difficult to be applied in actual usage. In this article, an efficient low-complexity CNN filter is proposed. We utilized depth separable convolution merged with the batch normalization as the backbone of our proposed CNN filter and presented a frame-level residual mapping (RM) to use one network to filter both intra- A nd intersamples. It is known that there will be an oversmoothing problem for the interframes if we directly use the filter trained with intrasamples. In this article, the proposed RM can effectively solve the oversmoothing problem. Besides, RM is flexible and can be combined with other learning-based filters. The experimental results show that our proposed method achieves a significant bjÃntegaard-delta(BD)-rate reduction than H.265/high efficiency video coding. The experiments show that the proposed network achieves about 1.2% BD-rate reduction and 79.1% decrease in FLOPs than VR-CNN. Our performance is better with less complexity than the previous work. The measurement on H.266/versatile video coding and ablation studies also ensure the effectiveness of the proposed method.

Original languageEnglish
Pages (from-to)83-93
Number of pages11
JournalIEEE Multimedia
Issue number2
Publication statusPublished - 2022

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Media Technology
  • Hardware and Architecture
  • Computer Science Applications


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