Abstract
In video transmissions over wired and wireless networks, packet losses occurs frequently due to channel errors and/or network latency, which may result in the whole video frame losses and severe video quality degradation. To address this problem, many error concealment algorithms have been proposed in order to reduce the effect of channel errors. However, most of these algorithms only focus on concealment of a missing block rather than a missing frame, which is far more difficult than the former since intra-frame information are no longer available in concealing the error. In this paper, we propose a practical multi-hypothesis based error concealment algorithm to recover the whole missing frame. This algorithm first reconstructs a motion vector field for each coding tree unit in the lost video frame according to the motion vector relationship between adjacent coding unit levels, and then the lost frame is reconstructed based on the theory of multi-hypothesis using each motion vector in the reconstructed motion vector field. The experimental results show that the proposed algorithm can effectively reconstruct the lost frames and significantly improve the video quality under frame loss.
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Acknowledgements
The authors would like to thank the anonymous reviewers for their potential valuable comments and suggestions that would help further improve the presentation of this paper. This work was partially supported by the National Key R&D Program of China (Grant No. 2016YFC0801001), the NSFC Key Project (No. 61632001) and the National Natural Science Foundation of China (No. 61772054, 61502278), Scientific Research Foundation of Shandong University of Science and Technology for Recruited Talents (No. 2015RCJJ067). This paper is partially done when Zhe Li was with Beijing Key Lab of Digital Media, School of Computer Science and Engineering, Beihang University, Beijing, China, 100191.
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Zhang, Y., Li, Z. (2018). Multi-hypothesis-Based Error Concealment for Whole Frame Loss in HEVC. In: Schoeffmann, K., et al. MultiMedia Modeling. MMM 2018. Lecture Notes in Computer Science(), vol 10704. Springer, Cham. https://doi.org/10.1007/978-3-319-73603-7_28
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DOI: https://doi.org/10.1007/978-3-319-73603-7_28
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