Constrained motion estimation-based error resilient coding for HEVC

Weihan Guo; Yongfei Zhang; Bo Li

doi:10.1117/12.2303602

10 April 2018 Constrained motion estimation-based error resilient coding for HEVC

Weihan Guo, Yongfei Zhang, Bo Li

Proceedings Volume 10615, Ninth International Conference on Graphic and Image Processing (ICGIP 2017); 1061542 (2018) https://doi.org/10.1117/12.2303602
Event: Ninth International Conference on Graphic and Image Processing, 2017, Qingdao, China

Abstract

Unreliable communication channels might lead to packet losses and bit errors in the videos transmitted through it, which will cause severe video quality degradation. This is even worse for HEVC since more advanced and powerful motion estimation methods are introduced to further remove the inter-frame dependency and thus improve the coding efficiency. Once a Motion Vector (MV) is lost or corrupted, it will cause distortion in the decoded frame. More importantly, due to motion compensation, the error will propagate along the motion prediction path, accumulate over time, and significantly degrade the overall video presentation quality. To address this problem, we study the problem of encoder-sider error resilient coding for HEVC and propose a constrained motion estimation scheme to mitigate the problem of error propagation to subsequent frames. The approach is achieved by cutting off MV dependencies and limiting the block regions which are predicted by temporal motion vector. The experimental results show that the proposed method can effectively suppress the error propagation caused by bit errors of motion vector and can improve the robustness of the stream in the bit error channels. When the bit error probability is 10^-5, an increase of the decoded video quality (PSNR) by up to1.310dB and on average 0.762 dB can be achieved, compared to the reference HEVC.

Citation Download Citation

Weihan Guo, Yongfei Zhang, and Bo Li "Constrained motion estimation-based error resilient coding for HEVC", Proc. SPIE 10615, Ninth International Conference on Graphic and Image Processing (ICGIP 2017), 1061542 (10 April 2018); https://doi.org/10.1117/12.2303602

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