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Selective motion vector redundancies for improved error resilience in HEVC

conference contribution
posted on 10.10.2016, 14:01 by Joao Carreira, Erhan EkmekciogluErhan Ekmekcioglu, Ahmet KondozAhmet Kondoz, Pedro A.A. Assuncao, Sergio M.M. Faria, Demuni V.S.X. De Silva
This paper addresses the problem caused by motion vector coding dependencies on the error resilience performance of the emergent High Efficiency Video Coding (HEVC) standard. We propose a method based on the prediction dependency of motion vectors (MV) to select the most relevant ones for redundant coding with reduced overhead. The spatial dependencies are analysed in the encoder to prioritise the MVs that should be selected for redundancy, based on the number of subsequent dependent coding units. Then, a subset of prioritised MVs is transmitted as redundancy (referred to as side information in the paper), to reduce the use and propagation of mismatched MV predictions in case of transmission errors or data loss. The simulation results show that the proposed MV selection method can effectively identify the most relevant motion field, achieving improved error robustness with a reduced redundancy overhead. Exploiting only 30% of the generated MVs for redundancy, average quality gains of up to 1 dB are achieved compared to a uniform MV selection scheme, and up to 2 dB compared to the original HEVC standard with no redundant encoded information.

History

School

  • Loughborough University London

Published in

Image Processing (ICIP), 2014 IEEE International Conference on

Pages

2457 - 2461

Citation

CARREIRA, J. ... et al., 2014. Selective motion vector redundancies for improved error resilience in HEVC. IN: Proceedings of 2014 IEEE International Conference on Image Processing (ICIP 2014), Paris, France, 27-30 October 2014, pp.2457-2461.

Publisher

© IEEE

Version

VoR (Version of Record)

Publisher statement

This work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/

Publication date

2014

Notes

Closed access.

ISBN

9781479957514

Language

en

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