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An AK-BRP dictionary learning algorithm for video frame sparse representation in compressed sensing

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Abstract

Sparsifying transform is an important prerequisite in compressed sensing. And it is practically significant to research the fast and efficient signal sparse representation methods. In this paper, we propose an adaptive K-BRP (AK-BRP) dictionary learning algorithm. The bilateral random projection (BRP), a method of low rank approximation, is used to update the dictionary atoms. Furthermore, in the sparse coding stage, an adaptive sparsity constraint is utilized to obtain sparse representation coefficient and helps to improve the efficiency of the dictionary update stage further. Finally, for video frame sparse representation, our adaptive dictionary learning algorithm achieves better performance than K-SVD dictionary learning algorithm in terms of computation cost. And our method produces smaller reconstruction error as well.

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Acknowledgments

This work was supported in part by National Natural Science Foundation of China (Granted No. 61070234, 61071167, 61373137, 61501251), university graduate student research innovation project of Jiangsu province in 2014 (Granted NO. KYZZ_0233) and in 2015 (Granted NO. KYZZ15_0235) and the NUPTSF (Granted No. NY214191).

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Authors and Affiliations

  1. School of Science, Nanjing University of Posts and Telecommunications, Nanjing, 210023, China

    Yang Qian & Feifei Zhou

  2. Center for Visual Cognitive Computation and Its Application, Nanjing University of Posts and Telecommunications, Nanjing, 210023, China

    Lei Li & Zhenzhen Yang

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  1. Yang Qian
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  2. Lei Li
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  3. Zhenzhen Yang
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  4. Feifei Zhou
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Correspondence to Yang Qian.

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Qian, Y., Li, L., Yang, Z. et al. An AK-BRP dictionary learning algorithm for video frame sparse representation in compressed sensing. Multimed Tools Appl 76, 23739–23755 (2017). https://doi.org/10.1007/s11042-016-4134-3

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  • DOI: https://doi.org/10.1007/s11042-016-4134-3

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