YANG Jia.Layered Refinement-based Wyner-Ziv Video Decoding Algorithm[J].Journal of Chengdu University of Information Technology,2022,37(01):62-66.[doi:10.16836/j.cnki.jcuit.2022.01.011]
基于分层细化的Wyner-Ziv视频解码算法
- Title:
- Layered Refinement-based Wyner-Ziv Video Decoding Algorithm
- 文章编号:
- 2096-1618(2022)01-0062-05
- Keywords:
- low-power communication; distributed video coding; virtual channel model; side information
- 分类号:
- TN919.81
- 文献标志码:
- A
- 摘要:
- 为进一步提高分布式视频编码(distributed video coding,DVC)的压缩性能,针对离散小波变换域DVC,提出了基于分层细化的Wyner-Ziv解码算法。算法充分利用小波多尺度和多分辨率的特性,将边信息优化算法和高阶统计模型进行了深度融合。在比特层面上,通过边信息优化算法提升每一分解层高频子带的边信息质量,从而提高高阶统计模型中与边信息相关的两大特征的准确性,增强高阶统计模型在信源相关性挖掘和有效利用方面的性能,实现DVC压缩性能的提升。测试结果表明,与参考文献相比,基于本文算法的DVC系统压缩性能有明显提高。
- Abstract:
- In order to further improve the compression performance for distributed video Coding(DVC), a layered refinement-based Wyner-Ziv video decoding algorithm is proposed for DVC in discrete wavelet transform(DWT)domain. The algorithm makes full use of the characteristics of wavelet multi-scale and multi-resolution in DWT domain to combine the side information optimization algorithm with the high-order statistical model in depth. At the bit-plane level, the side information quality of each high frequency sub-band is refined by the optimization algorithm, and then the optimized side information is used to promote the accuracy of the two features related to side information in the high-order statistical model. The performance of high-order statistical model in the effective exploration and utilization of the source correlation is thus enhanced, and the improvement of DVC compression performance is achieved. The test results show that, compared with the references, the compression performance of the DVC system based on the proposed algorithm is improved obviously.
参考文献/References:
[1] Slepian D,Wolf K.Noiseless Coding of Correlated Information Sources[J].IEEE Transactions on Information Theory,1973,19(4):471-480.
[2] Wyner A D,Ziv J.The Rate Distortion Function for Source Coding with Side Information at the Decoder[J].IEEE Transactions on Information Theory,1979,22(1):1-10.
[3] Martins R,Brites C,Ascenso J,et al.Statistical Motion Learning for Improved Transform Domain Wyner-Ziv Video Coding[J].IET Image Processing,2010,4(1):28-41.
[4] Ascenso J,Brites C,Pereira F.A Denoising Approach for Iterative Side Information Creation in Distributed Video Coding [C]. IEEE International Conference on Image Processing,Brussels,2011:3513-3516.
[5] Luong H V,Rakêt L L,Forchhammer S.Re-estimation of Motion and Reconstruction for Distributed Video Coding[J].IEEE Transactions on Image Processing,2014,23(7):2804-2819.
[6] Abou-Elailah A,Dufaux F,Farah J,et al.Fusion of Global and Local Motion Estimation for Distributed Video Coding[J].IEEE Transactions on Circuits and Systems for Video Technology,2013,23(1):158-172.
[7] Di J,Hua H.An Improved Side Information Generation Scheme for Wyner-Ziv Video Coding[C]. 2010 International Conference on Computational Intelligence and Security, Nanning,2010:234-237.
[8] Fang S,Yuan C,Zhong Y.Refining Side Information by ODWT MCTI for Wyner-Ziv Video Coding [C].2010 International Symposium on Intelligent Signal Processing and Communication Systems, Chengdu,2010:1-4.
[9] Liu W,Dong L,Zeng W. Motion Refinement Based Progressive Side-Information Estimation for Wyner-Ziv Video Coding[J].IEEE Transactions on Circuits and Systems for Video Technology,2010,20(12):1863-1875.
[10] Yang J,He X,Qing L,et al.A New Progressively Refined Wyner-Ziv Video Coding for Low-Power Human-Centered Telehealth[J].IEEE Access,2018,6(10):38315-38325.
[11] Fan X,Au O C,Cheung N M.Transform-Domain Adaptive Correlation Estimation(TRACE)for Wyner-Ziv Video Coding[J].IEEE Transactions on Circuits and Systems for Video Technology,2010,20(11):1423-1436.
[12] Song J,Wang K,Liu H,et al.Progressive Correlation Noise Refinement for Transform Domain Wyner-Ziv Video Coding[C].2011 18th IEEE International Conference on Image Processing,Brussels,2011:2625-2628.
[13] Huang X,Forchhammer S.Cross-Band Noise Model Refinement for Transform Domain Wyner-Ziv Video Coding[J].Signal Processing:Image Communication,2012,27(1):16-30.
[14] Deligiannis N,Munteanu A,Wang S,et al.Maximum Likelihood Laplacian Correlation Channel Estimation in Layered Wyner-Ziv Coding[J].IEEE Transactions on Signal Processing,2014,62(4):892-904.
[15] Wang S,Cui L,Stankovic L,et al.Adaptive Correlation Estimation with Particle Filtering for Distributed Video Coding[J].IEEE Transactions on Circuits and Systems for Video Technology,2012,22(5):649-658.
[16] Qing L,Zeng W.Improving Distributed Video Coding by Exploiting Context-Adaptive Modeling[C].2014 IEEE International Conference on Multimedia and Expo(ICME),Chengdu,2014:1-6.
[17] Qing L,Zeng W.Context-Adaptive Modeling for Wavelet-Domain Distributed Video Coding[J].IEEE Multimedia,2014,21(4):84-93.
[18] YANG J,Qing L,Zeng W,He X.High-Order Statistical Modeling Based on a Decision Tree for Distributed Video Coding[J].IEEE Transactions on Circuits and Systems for Video Technology,2019,29(5):1488-1502.
[19] Kubasov D,Nayak J,Guillemot C.Optimal Reconstruction in Wyner-Ziv Video Coding with Multiple Side Information[C].2007 IEEE 9th Workshop on Multimedia Signal Processing,Crete,2007:183-186.
[20] Bjøntegaard G.Calculation of Average PSNR Differences Between RD-curves[S].Austin,TX,ITU-T SG16/Q.6,Doc.VCEG-M033,Apr.2001.
备注/Memo
收稿日期:2021-07-15
基金项目:四川省教育厅科研基金资助项目(16ZB0220)