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Band-Level Correlation Noise Modeling for Wyner–Ziv Video Coding with Gaussian Mixture Models

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Abstract

As one of the most adopted distributed video coding approaches in the literature, Wyner–Ziv (WZ) video coding is not yet on par with the motion-compensated predictive coding solutions with respect to rate–distortion (RD) performance. One of the essential reasons lies in the absence of reliable knowledge of the correlation statistics between source and side information. Most of the existing works assume a probability distribution of the statistical dependency to be Laplacian, which is not accurate but computationally cheap. In this paper, a correlation estimation based on Gaussian mixture model is proposed for the band-level correlation noise of discrete cosine transform domain Wyner–Ziv codec. The statistics of the correlation noise between WZ frame and corresponding side information is analyzed by considering the temporal correlation and quantization distortion. Accordingly, the model parameters for correlation noise are estimated offline and utilized online in consequent decoding. The simulation results of Kullback–Leibler divergence show that the proposed model has higher accuracy than the Laplacian one. Experimental results demonstrate that the WZ codec incorporated with the proposed model can achieve very competitive RD performance, especially for the sequence with high motion contents and large group of picture (GOP) size.

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Acknowledgments

The work of the second author was partially supported by the Commonwealth of Australian under the Australian-China Science and Research Fund (ACSRF01222) and the Australian Research Council (ARC) under Discovery Project Grant DP130100364. The work was also supported by the National Science Foundation of China, under Grant 61201392.

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Authors and Affiliations

  1. School of Automation, Guangdong University of Technology, Guangzhou, 510006, China

    Ming Yin & Shuting Cai

  2. School of Computing and Mathematics, Charles Sturt University, Bathurst, NSW, 2795, Australia

    Junbin Gao

  3. School of Engineering and Information Sciences, Middlesex University, London, NW4 4BT, UK

    Daming Shi

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  1. Ming Yin
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  2. Junbin Gao
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  3. Daming Shi
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  4. Shuting Cai
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Correspondence to Ming Yin.

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Yin, M., Gao, J., Shi, D. et al. Band-Level Correlation Noise Modeling for Wyner–Ziv Video Coding with Gaussian Mixture Models. Circuits Syst Signal Process 34, 2237–2254 (2015). https://doi.org/10.1007/s00034-014-9951-x

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