Distributed Video Compressive Sensing Reconstruction by Adaptive PCA Sparse Basis and Nonlocal Similarity

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2017.09.07
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2017.09
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서지정보

발행기관 : 한국인터넷정보학회 수록지정보 : KSII Transactions on Internet and Information Systems (TIIS) / 8권 / 8호
저자명 : ( Wu Minghu ) , ( Zhu Xiuchang )

영어 초록

To improve the rate-distortion performance of distributed video compressive sensing (DVCS), the adaptive sparse basis and nonlocal similarity of video are proposed to jointly reconstruct the video signal in this paper. Due to the lack of motion information between frames and the appearance of some noises in the reference frames, the sparse dictionary, which is constructed using the examples directly extracted from the reference frames, has already not better obtained the sparse representation of the interpolated block. This paper proposes a method to construct the sparse dictionary. Firstly, the example-based data matrix is constructed by using the motion information between frames, and then the principle components analysis (PCA) is used to compute some significant principle components of data matrix. Finally, the sparse dictionary is constructed by these significant principle components. The merit of the proposed sparse dictionary is that it can not only adaptively change in terms of the spatial-temporal characteristics, but also has ability to suppress noises. Besides, considering that the sparse priors cannot preserve the edges and textures of video frames well, the nonlocal similarity regularization term has also been introduced into reconstruction model. Experimental results show that the proposed algorithm can improve the objective and subjective quality of video frame, and achieve the better rate-distortion performance of DVCS system at the cost of a certain computational complexity.

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Distributed Video Compressive Sensing Reconstruction by Adaptive PCA Sparse Basis and Nonlocal Similarity