Neural Network-based Error Concealment for B-Frames in VVC

Martin Benjak, Niklas Aust, Yasser Samayoa, Jörn Ostermann

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.