Name
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. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/MMUL.2022.3159372 | IEEE MultiMedia |
Keywords | DocType | Volume |
video artifacts reduction,low-complexity CNN filter,frame-level residual mapping,oversmoothing problem,learning-based filters,BD-rate reduction,low-complexity convolutional neural network filter,frame-level RM,bjontegaard-delta-rate reduction,batch normalization | Journal | 29 |
Issue | ISSN | Citations |
2 | 1070-986X | 0 |
PageRank | References | Authors |
0.34 | 9 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chao Liu | 1 | 0 | 0.34 |
| Heming Sun | 2 | 92 | 22.50 |
| Jiro Katto | 3 | 262 | 66.14 |
| Xiaoyang Zeng | 4 | 442 | 107.26 |
| Yibo Fan | 5 | 8 | 7.71 |