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Nonlocally Adaptive Pattern Classification Based Compressed Sensing for Video Recovery

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Abstract

Traditional video coding algorithms usually undergo several coding steps for each frame before transmission, which reduces the efficiency at the encoder. The compressed sensing (CS) as an innovative method in signal processing can make the encoder much easier than ever before, with which each frame only needs to multiply a projection matrix at the encoder, if the frame is sparse in a transform domain. Frames in a video usually exhibit sparsity in different parts on different bases; however, existing compressed sensing reconstruction methods usually recover a frame in a fixed set of bases for the entirety of the frame. Therefore, the frames cannot be recovered faithfully by the conventional CS reconstruction methods from a small number of measurements. In this paper, in order to rectify the flaw, we construct an initial estimation frame by motion estimation from neighboring frames and through the observation of the current frame. Then, nonlocally adaptive sparse signal presentation facilitation by a 2D piecewise autoregressive (AR) model is integrated into the reconstruction. The piecewise AR model is generated from the pattern classification of subimages of the initial estimation frame and its neighboring frames. An iterative procedure is proposed to recover a new estimated frame and its AR model alternatively, until the termination threshold is satisfied. The experimental results demonstrating the capabilities of the proposed method are presented.

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Authors and Affiliations

  1. School of Electronic Information, Wuhan University, Wuhan, 430072, China

    Wenxuan Shi & Jie Li

  2. Institute of Microelectronics and Information Technology, Wuhan University, Wuhan, 430072, China

    Xi Chen

Authors
  1. Wenxuan Shi
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  2. Xi Chen
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  3. Jie Li
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Correspondence to Xi Chen.

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Shi, W., Chen, X. & Li, J. Nonlocally Adaptive Pattern Classification Based Compressed Sensing for Video Recovery. Circuits Syst Signal Process 33, 241–256 (2014). https://doi.org/10.1007/s00034-013-9636-x

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  • DOI: https://doi.org/10.1007/s00034-013-9636-x

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