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Residual domain dictionary learning for compressed sensing video recovery

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Abstract

For compressed sensing (CS) recovery, the reconstruction quality is highly dependent on the sparsity level of the representation for the signal. Motivated by the observation that the temporal residual image is much sparser than its original image, a temporal residual-domain dictionary learning method for CS video recovery is proposed in this paper. The adaptive basis is learned from inter-frame differences by Karhunen–Loeve transform (KLT) to represent the residuals. And a block-based motion estimation/motion compensation (ME/MC) residual reconstruction strategy is incorporated for the CS video recovery. Experimental results on common test sequences at various sampling rates illustrate that the proposed algorithm gains great improvements over existing approaches. For some video sequences, the proposed method outperforms the state-of-art method near 1 dB in terms of peak signal noise rate (PSNR) at some higher sampling rate.

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Acknowledgments

This work was supported in part by the Hunan Province Science and Technology Planning Project (nos. 2014FJ6047 and 2014GK3030), the Science Research Key Project of the Education Department of Hunan Province (nos. 13A107 and 15A007), and the Changsha Science and Technology Planning Project (no. K1403028-11).

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Authors and Affiliations

  1. School of Information Science & Engineering, Hunan University, Changsha, 410082, China

    Yun Song, Gaobo Yang & Dengyong Zhang

  2. School of Computer and Communication Engineering, Changsha University of Science & Technology, Changsha, 410114, China

    Yun Song & Dengyong Zhang

  3. Institute of Information Engineering, Chinese Academy of Sciences, Beijing, 100093, China

    Hongtao Xie

  4. School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, 210044, China

    Sun Xingming

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  1. Yun Song
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  2. Gaobo Yang
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  3. Hongtao Xie
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  4. Dengyong Zhang
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  5. Sun Xingming
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Correspondence to Gaobo Yang.

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Song, Y., Yang, G., Xie, H. et al. Residual domain dictionary learning for compressed sensing video recovery. Multimed Tools Appl 76, 10083–10096 (2017). https://doi.org/10.1007/s11042-016-3599-4

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  • DOI: https://doi.org/10.1007/s11042-016-3599-4

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