Background Error Propagation Model Based RDO in HEVC for Surveillance and Conference Video Coding

The emerging high efficiency video coding (HEVC) Standard has significantly improved the compression performance in comparison with its predecessor H.264/AVC. However, it was originally designed for generic video contents. The backgrounds are generally static in the surveillance and conference videos. The background coding errors will propagate to the subsequent frames in coding the videos. In this paper, a background error propagation (BEP) model-based rate distortion optimization (RDO) scheme in HEVC is proposed for the surveillance and conference videos. First, the R-D performance of the long-term frames is optimized globally. The global RDO scheme can efficiently exploit the background error propagation. Second, a BEP model is studied to express the linear relationship between the distortion of the first frame and that of its subsequent frames. Based on the BEP model, enhanced frames are proposed to be coded with a small quantization parameter so as to improve the global performance. Third, a decay model is proposed to investigate the variation of the error propagation ratio as the frame order increased. Based on the decay model, a periodical optimization scheme is presented by deploying the enhanced frames periodically. Experiments are tested on the surveillance and conference videos. The results show that the proposed algorithm achieves significant performance improvement.

[1]  Ajay Luthra,et al.  Overview of the H.264/AVC video coding standard , 2003, IEEE Trans. Circuits Syst. Video Technol..

[2]  Xianguo Zhang,et al.  Optimizing the Hierarchical Prediction and Coding in HEVC for Surveillance and Conference Videos With Background Modeling , 2014, IEEE Transactions on Image Processing.

[3]  Hongliang Li,et al.  MRF-Based Fast HEVC Inter CU Decision With the Variance of Absolute Differences , 2014, IEEE Transactions on Multimedia.

[4]  Wen Gao,et al.  An efficient foreground-based surveillance video coding scheme in low bit-rate compression , 2012, 2012 Visual Communications and Image Processing.

[5]  Jeong-Hoon Park,et al.  Block Partitioning Structure in the HEVC Standard , 2012, IEEE Transactions on Circuits and Systems for Video Technology.

[6]  John Gantz,et al.  The Digital Universe in 2020: Big Data, Bigger Digital Shadows, and Biggest Growth in the Far East , 2012 .

[7]  Dong Liu,et al.  Surveillance video coding with vehicle library , 2017, 2017 IEEE International Conference on Image Processing (ICIP).

[8]  Hongliang Li,et al.  A Fast HEVC Inter CU Selection Method Based on Pyramid Motion Divergence , 2014, IEEE Transactions on Multimedia.

[9]  G. Bjontegaard,et al.  Calculation of Average PSNR Differences between RD-curves , 2001 .

[10]  Chongyu Chen,et al.  Incremental low-rank and sparse decomposition for compressing videos captured by fixed cameras , 2015, J. Vis. Commun. Image Represent..

[11]  Antti Hallapuro,et al.  High Performance, Low Complexity Video Coding and the Emerging HEVC Standard , 2010, IEEE Transactions on Circuits and Systems for Video Technology.

[12]  Hongliang Li,et al.  A Perceptually Weighted Rank Correlation Indicator for Objective Image Quality Assessment , 2017, IEEE Transactions on Image Processing.

[13]  Min-Su Cheon,et al.  Improved Video Compression Efficiency Through Flexible Unit Representation and Corresponding Extension of Coding Tools , 2010, IEEE Transactions on Circuits and Systems for Video Technology.

[14]  Xianguo Zhang,et al.  Background-Modeling-Based Adaptive Prediction for Surveillance Video Coding , 2014, IEEE Transactions on Image Processing.

[15]  Xianguo Zhang,et al.  A background proportion adaptive Lagrange multiplier selection method for surveillance video on HEVC , 2013, 2013 IEEE International Conference on Multimedia and Expo (ICME).

[16]  Dong Liu,et al.  Surveillance video coding with dynamic textural background detection , 2017, 2017 IEEE International Conference on Image Processing (ICIP).

[17]  Kai-Kuang Ma,et al.  ESIM: Edge Similarity for Screen Content Image Quality Assessment , 2017, IEEE Transactions on Image Processing.

[18]  Jing Chen,et al.  Perceptual feature guided rate distortion optimization for high efficiency video coding , 2017, Multidimens. Syst. Signal Process..

[19]  Xianguo Zhang,et al.  Macro-Block-Level Selective Background Difference Coding for Surveillance Video , 2012, 2012 IEEE International Conference on Multimedia and Expo.

[20]  Chongyu Chen,et al.  Surveillance video coding via low-rank and sparse decomposition , 2012, ACM Multimedia.

[21]  Zixiang Xiong,et al.  Knowledge-Based Coding of Objects for Multisource Surveillance Video Data , 2016, IEEE Transactions on Multimedia.

[22]  Ruimin Hu,et al.  A Block-Based Background Model for Surveillance Video Coding , 2015, 2015 Data Compression Conference.

[23]  King Ngi Ngan,et al.  Fast HEVC Inter CU Decision Based on Latent SAD Estimation , 2015, IEEE Transactions on Multimedia.

[24]  Tiejun Huang,et al.  Background-foreground division based search for motion estimation in surveillance video coding , 2014, 2014 IEEE International Conference on Multimedia and Expo (ICME).

[25]  King Ngi Ngan,et al.  Blind Image Quality Assessment Based on Rank-Order Regularized Regression , 2017, IEEE Transactions on Multimedia.

[26]  Gary J. Sullivan,et al.  Overview of the High Efficiency Video Coding (HEVC) Standard , 2012, IEEE Transactions on Circuits and Systems for Video Technology.

[27]  King Ngi Ngan,et al.  Blind Image Quality Assessment Based on Multichannel Feature Fusion and Label Transfer , 2016, IEEE Transactions on Circuits and Systems for Video Technology.

[28]  Wen Gao,et al.  Intelligent analysis oriented surveillance video coding , 2017, 2017 IEEE International Conference on Multimedia and Expo (ICME).

[29]  Li Xiujuan,et al.  Background-foreground information based bit allocation algorithm for surveillance video on high efficiency video coding (HEVC) , 2016 .