Neural Network-Based Error Concealment For VVC

Martin Benjak, Yasser Samayoa, Jörn Ostermann

Abstract: In this paper we introduce an error concealment method for VVC based on deep recurrent neural networks, which employs the PredNet model to estimate missing video frames by using past decoded frames. The network is trained using the BVI-DVC data set to infer even full-HD frames. We integrated our proposed model in the VVC reference software VTM for its evaluation. It performs, in average, 6 dB or up to 5 dB better than the frame copy model in terms of PSNR measurements for a concealed I-frame or P-frame, respectively.