Spatial error concealment by jointing gauss bayes model and SVD for high efficiency video coding

Mingliang ZHOU, Qin MAO*, Chen ZHONG, Weiqin ZHANG, Changzhi CHEN

*Corresponding author for this work

Research output: Journal PublicationsJournal Article (refereed)peer-review

1 Citation (Scopus)

Abstract

This paper proposes a novel sparsity-based error concealment (EC) algorithm which integrates the Gauss Bayes model and singular value decomposition for high efficiency video coding (HEVC). Under the sequential recovery framework, pixels in missing blocks are successively reconstructed in Gauss Bayes mode. We find that the estimation error follows the Gaussian distribution in HEVC, so the error pixel estimation problem can be transferred to a Bayesian estimation. We utilize the singular value decomposition (SVD) technique to select sample pixels, which yields high estimation accuracy and reduces estimation error. A new recovery order based on confidence is established to resolve the error propagation problem. Compared to other state-of-the-art EC algorithms, experimental results show that the proposed method gives better reconstruction performance in terms of objective and subjective evaluations. It also has significantly lower complexity.

Original languageEnglish
Article number1954037
JournalInternational Journal of Pattern Recognition and Artificial Intelligence
Volume33
Issue number14
Early online date15 May 2019
DOIs
Publication statusPublished - 31 Dec 2019
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2019 World Scientific Publishing Company.

Keywords

  • Confidence
  • Error concealment
  • Gaussian process regression
  • SVD

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