Name
Abstract | ||
|---|---|---|
High efficiency video coding (HEVC) has brought outperforming efficiency for video compression. To reduce the compression artifacts of HEVC, we propose a DenseNet based approach as the in-loop filter of HEVC, which leverages multiple adjacent frames to enhance the quality of each encoded frame. Specifically, the higher-quality frames are found by a reference frame selector (RFS). Then, a deep neural network for multi-frame in-loop filter (named MIF-Net) is developed to enhance the quality of each encoded frame by utilizing the spatial information of this frame and the temporal information of its neighboring higher-quality frames. The MIF-Net is built on the recently developed DenseNet, benefiting from the improved generalization capacity and computational efficiency. Finally, experimental results verify the effectiveness of our multi-frame in-loop filter, outperforming the HM baseline and other state-of-the-art approaches. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/DCC.2019.00035 | 2019 Data Compression Conference (DCC) |
Keywords | DocType | Volume |
high efficiency video coding,in loop filter,DenseNet,multiple frames | Journal | abs/1903.01648 |
ISSN | ISBN | Citations |
1068-0314 | 978-1-7281-0658-8 | 4 |
PageRank | References | Authors |
0.51 | 16 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tianyi Li | 1 | 53 | 3.47 |
| Mai Xu | 2 | 509 | 57.90 |
| Ren Yang | 3 | 64 | 8.19 |
| Xiaoming Tao | 4 | 321 | 53.93 |