Video Error Concealment Using Deep Neural Networks

Arun Sankisa, Arjun Punjabi, Aggelos K. Katsaggelos

Research output: Chapter in Book/Report/Conference proceedingConference contribution

17 Scopus citations


This paper presents an adaptable decoder-like model for video error concealment through optical flow prediction using deep neural networks. The horizontal and vertical motion fields from previous optical flows are separated and passed through two parallel pipelines with convolutional and long short-term memory layers. The combined output from these two networks, the predicted flow, is then used to reconstruct the degraded portion of the future video frame. Unlike current methods that use pixel or voxel information, we propose an architecture that uses three previous optical flows obtained through a flow generation step. The generator portion of the network can be easily interchanged with other methods, increasing the adaptability of the model. The network is trained in supervised mode and the performance is evaluated using standard video quality metrics by comparing the reconstructed frames from our prediction and the generated ground truth.

Original languageEnglish (US)
Title of host publication2018 IEEE International Conference on Image Processing, ICIP 2018 - Proceedings
PublisherIEEE Computer Society
Number of pages5
ISBN (Electronic)9781479970612
StatePublished - Aug 29 2018
Event25th IEEE International Conference on Image Processing, ICIP 2018 - Athens, Greece
Duration: Oct 7 2018Oct 10 2018

Publication series

NameProceedings - International Conference on Image Processing, ICIP
ISSN (Print)1522-4880


Conference25th IEEE International Conference on Image Processing, ICIP 2018


  • CNN
  • ConvLSTM
  • Deep Neural Networks
  • Optical flow
  • Video Error Concealment

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing


Dive into the research topics of 'Video Error Concealment Using Deep Neural Networks'. Together they form a unique fingerprint.

Cite this