Neural Network-Based Error Concealment For VVC

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 pro...

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Bibliographic Details
Main Authors: Benjak, Martin, Samayoa, Yasser, Ostermann, Jorn
Format: Conference Proceeding
Language:English
Subjects:
Communication systems
Conferences
error concealment
Image resolution
Recurrent neural networks
Software algorithms
Video coding
video communication
Visualization
VVC
Online Access:Request full text
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Summary: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.
ISSN:2381-8549
DOI:10.1109/ICIP42928.2021.9506399