Spatial Error Concealment by Jointing Gauss Bayes Model and SVD for High Efficiency Video Coding

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.

A multi-level approach with visual information for encrypted H.265/HEVC videos

“…More details of CAVLC are described in the work [40]. In the coding process, TotalCoeffs and TrailingOnes cannot be encrypted because they will lead to decoding failure [41]. In the proposed scheme, the sign of the TrailingOnes is to be encrypted.…”