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Presenter(s)
![Martin Benjak Headshot](https://confcats-catavault.s3.amazonaws.com/CATAVault/ieeecass/master/files/styles/cc_user_photo/s3/user-pictures/11631.jpg?h=b12eb09c&itok=e-OJQ5Zr)
Display Name
Martin Benjak
- Affiliation
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AffiliationLeibniz Universität Hannover
- Country
Abstract
In this paper we introduce an error concealment method for VVC that error-conceals B-frames based on the neural frame interpolation network RIFE. The network is trained using the BVI-DVC dataset to infer even full-HD frames. We integrate our proposed model in the VVC reference software VTM for its evaluation. The average error of a whole GOP with a single corrupted frame is decreased by 15% and 24% in terms of PSNR measurement compared to block matching and frame copy, respectively. To our knowledge, our approach is currently the best performing error concealment algorithm for single slice per B-frame settings.