SSIM-Based Error Resilient Video Coding over Packet-Switched Networks

The visual quality is a critical factor of compressed videos for error-prone transmission. This paper proposes a structural similarity (SSIM) based error resilient video coding scheme to improve the visual quality of compressed videos for transmission over packet-switched networks. In the proposed scheme, a SSIM-based end-to-end distortion model is developed to estimate the perceptual distortion for spatial and temporal error propagation. Based on the model, an optimal mode selection strategy is presented to improve the rate-distortion performance. Experiments show that the proposed scheme significantly improves the visual quality for H.264/AVC video coding over packet-switched networks.

[1]  Homer H. Chen,et al.  Improving video coding quality by perceptual rate-distortion optimization , 2010, 2010 IEEE International Conference on Multimedia and Expo.

[2]  Yixuan Zhang,et al.  A Joint Source-Channel Video Coding Scheme Based on Distributed Source Coding , 2008, IEEE Transactions on Multimedia.

[3]  Jonathan Loo,et al.  Error-Resilient Scheme for Wavelet Video Codec Using Automatic ROI Detection and Wyner-Ziv Coding Over Packet Erasure Channel , 2010, IEEE Transactions on Broadcasting.

[4]  Yutaka Ishibashi,et al.  Efficient error resilient algorithm for H.264/AVC: mobility management in wireless video streaming , 2009, Telecommun. Syst..

[5]  Yao Wang,et al.  Modeling of transmission-loss-induced distortion in decoded video , 2006, IEEE Transactions on Circuits and Systems for Video Technology.

[6]  Ran Giladi,et al.  New Error-Resilient Scheme Based on FMO and Dynamic Redundant Slices Allocation for Wireless Video Transmission , 2007, IEEE Transactions on Broadcasting.

[7]  Wen Gao,et al.  SSIM-Motivated Rate-Distortion Optimization for Video Coding , 2012, IEEE Transactions on Circuits and Systems for Video Technology.

[8]  Yao Zhao,et al.  Joint redundant motion vector and intra macroblock refreshment for video transmission , 2011, EURASIP J. Image Video Process..

[9]  Eero P. Simoncelli,et al.  Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.

[10]  Feng Wu,et al.  Channel Distortion Modeling for Multi-View Video Transmission Over Packet-Switched Networks , 2011, IEEE Transactions on Circuits and Systems for Video Technology.

[11]  Rui Zhang,et al.  Video coding with optimal inter/intra-mode switching for packet loss resilience , 2000, IEEE Journal on Selected Areas in Communications.

[12]  Homer H. Chen,et al.  SSIM-Based Perceptual Rate Control for Video Coding , 2011, IEEE Transactions on Circuits and Systems for Video Technology.

[13]  Thomas Wiegand,et al.  Lagrange multiplier selection in hybrid video coder control , 2001, Proceedings 2001 International Conference on Image Processing (Cat. No.01CH37205).

[14]  Stewart T. Worrall,et al.  Error resilience for multi-view video using redundant macroblock coding , 2011, 2011 6th International Conference on Industrial and Information Systems.

[15]  Jianfei Cai,et al.  Joint source channel rate-distortion analysis for adaptive mode selection and rate control in wireless video coding , 2002, IEEE Trans. Circuits Syst. Video Technol..

[16]  Zhihai He,et al.  An Error Resilient Video Coding Scheme Using Embedded Wyner–Ziv Description With Decoder Side Non-Stationary Distortion Modeling , 2011, IEEE Transactions on Circuits and Systems for Video Technology.

[17]  Zhan Ma,et al.  Perceptual Quality Assessment of Video Considering Both Frame Rate and Quantization Artifacts , 2011, IEEE Transactions on Circuits and Systems for Video Technology.

[18]  Rajiv Soundararajan,et al.  Study of Subjective and Objective Quality Assessment of Video , 2010, IEEE Transactions on Image Processing.