Abstract
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- and 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 bjøntegaard-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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.