TY - GEN
T1 - Video Error Concealment Using Deep Neural Networks
AU - Sankisa, Arun
AU - Punjabi, Arjun
AU - Katsaggelos, Aggelos K.
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/8/29
Y1 - 2018/8/29
N2 - 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.
AB - 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.
KW - CNN
KW - ConvLSTM
KW - Deep Neural Networks
KW - Optical flow
KW - Video Error Concealment
UR - http://www.scopus.com/inward/record.url?scp=85062912526&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85062912526&partnerID=8YFLogxK
U2 - 10.1109/ICIP.2018.8451090
DO - 10.1109/ICIP.2018.8451090
M3 - Conference contribution
AN - SCOPUS:85062912526
T3 - Proceedings - International Conference on Image Processing, ICIP
SP - 380
EP - 384
BT - 2018 IEEE International Conference on Image Processing, ICIP 2018 - Proceedings
PB - IEEE Computer Society
T2 - 25th IEEE International Conference on Image Processing, ICIP 2018
Y2 - 7 October 2018 through 10 October 2018
ER -