Name
Title | ||
|---|---|---|
Overfitting multiplier parameters for content-adaptive post-filtering in video coding |
Abstract | ||
|---|---|---|
Previous works have shown that artefacts generated by lossy video compression can be reduced by content-adaptive filtering via overfitting Neural Network (NN) filters on test content, and signalling a compressed weight update. This approach is applied to post-processing filtering, and three main contributions are proposed. Firstly, a new set of learnable parameters, named multipliers, are incorporated into the NNs, and only those are overfitted. Secondly, a new training scheme is proposed for jointly training multiple NNs, and used to adapt the weights of an efficient NN architecture (originally designed for in-loop filters) to function as post-filters. Thirdly, the weight update is signalled via a newly-designed Supplemental Enhancement Information (SEI) message. The proposed post-filter saved about 5.01% (Y), 18.95% (Cb), 17.33% (Cr) Bjøntegaard Delta rate (BD-rate) on top of the Versatile Video Coding (VVC) Test Model (VTM) 11.0 NN-based Video Coding (NNVC) 1.0, Random Access (RA) Common Test Conditions (CTC). Additional experiments provide comparisons of overfitting different parameters and trade-offs between overfitting time and coding gains. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/EUVIP53989.2022.9922721 | 2022 10th European Workshop on Visual Information Processing (EUVIP) |
Keywords | DocType | ISSN |
Overfitting,post-filter,video coding,VVC,neural network | Conference | 2164-974X |
ISBN | Citations | PageRank |
978-1-6654-6624-0 | 0 | 0.34 |
References | Authors | |
15 | 8 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Maria Santamaria | 1 | 0 | 0.34 |
| Ruiying Yang | 2 | 0 | 0.34 |
| Francesco Cricri | 3 | 1 | 1.71 |
| Honglei Zhang | 4 | 0 | 0.34 |
| Jani Lainema | 5 | 1 | 0.70 |
| Ramin G. Youvalari | 6 | 1 | 1.37 |
| Hamed R. Tavakoli | 7 | 0 | 0.34 |
| M. M. Hannuksela | 8 | 376 | 80.61 |