A Bidirectional Best Matching Algorithm Based on Distributed Compressed Video Sensing

Reconstruction technology of Distributed Compressed Video Sensing (DCVS) based on Multi-hypothesis (MH) Predictions is currently a better video reconstruction scheme. In this paper, a method using bidirectional best matching (BBM) process constrained by target tracking for pixel block structure similarity in the measurement domain optimizing the MH prediction model is proposed to generate side information (SI), and the residual compensation is combined to reconstruct the nonkey frame. Simulation experiment results show that the average Peak Signal to Noise Ratio (PSNR) of reconstructed method is 1.97dB higher than that of the other schemes at the same sampling rate, and a better subjective visual effect can be obtained with an 11% increase of reconstruction time representing algorithm complexity.

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