Name
Abstract | ||
|---|---|---|
We present an efficient finetuning methodology for neural-network filters which are applied as a postprocessing artifact-removal step in video coding pipelines. The fine-tuning is performed at encoder side to adapt the neural network to the specific content that is being encoded. In order to maximize the PSNR gain and minimize the bitrate overhead, we propose to finetune only the convolutional layers' biases. The proposed method achieves convergence much faster than conventional finetuning approaches, making it suitable for practical applications. The weight-update can be included into the video bitstream generatedby the existing video codecs. We show that our method achieves up to 9.7% average BD-rate gain when compared to the state-of-art Versatile Video Coding (VVC) standard codec on 7 test sequences.
|
Year | DOI | Venue |
|---|---|---|
2020 | 10.1145/3394171.3413536 | MM '20: The 28th ACM International Conference on Multimedia
Seattle
WA
USA
October, 2020 |
DocType | ISBN | Citations |
Conference | 978-1-4503-7988-5 | 2 |
PageRank | References | Authors |
0.43 | 3 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yat-Hong Lam | 1 | 2 | 0.43 |
| Alireza Zare | 2 | 62 | 7.23 |
| Francesco Cricri | 3 | 64 | 11.77 |
| Jani Lainema | 4 | 415 | 39.62 |
| M. M. Hannuksela | 5 | 376 | 80.61 |