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Residual Reconstruction Algorithm Based on Half-Pixel Multi-Hypothesis Prediction for Distributed Compressive Video Sensing

Residual Reconstruction Algorithm Based on Half-Pixel Multi-Hypothesis Prediction for Distributed Compressive Video Sensing

Ying Tong, Rui Chen, Jie Yang, Minghu Wu
Copyright: © 2018 |Volume: 9 |Issue: 4 |Pages: 18
ISSN: 1937-9412|EISSN: 1937-9404|EISBN13: 9781522543312|DOI: 10.4018/IJMCMC.2018100102
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MLA

Tong, Ying, et al. "Residual Reconstruction Algorithm Based on Half-Pixel Multi-Hypothesis Prediction for Distributed Compressive Video Sensing." IJMCMC vol.9, no.4 2018: pp.16-33. http://doi.org/10.4018/IJMCMC.2018100102

APA

Tong, Y., Chen, R., Yang, J., & Wu, M. (2018). Residual Reconstruction Algorithm Based on Half-Pixel Multi-Hypothesis Prediction for Distributed Compressive Video Sensing. International Journal of Mobile Computing and Multimedia Communications (IJMCMC), 9(4), 16-33. http://doi.org/10.4018/IJMCMC.2018100102

Chicago

Tong, Ying, et al. "Residual Reconstruction Algorithm Based on Half-Pixel Multi-Hypothesis Prediction for Distributed Compressive Video Sensing," International Journal of Mobile Computing and Multimedia Communications (IJMCMC) 9, no.4: 16-33. http://doi.org/10.4018/IJMCMC.2018100102

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Abstract

Compressed sensing (CS) provides a method to sample and reconstruct sparse signals far below the Nyquist sampling rate, which has great potential in image/video acquisition and processing. In order to fully exploit the spatial and temporal characteristics of video frame and the coherence between successive frames, we propose a half-pixel interpolation based residual reconstruction method for distributed compressive video sensing (DCVS). At the decoding end, half-pixel interpolation and bi-directional motion estimation helps refine the side information for joint decoding of the non-key-frames. We apply a multi-hypothesis based on residual reconstruction algorithms to reconstruct the non-key-frames. Performance analysis and simulation experiments show that the quality of side information generated by the proposed algorithm is increased by about 1.5dB, with video reconstruction quality increased 0.3~2dB in PSNR, when compared with prior works on DCVS.

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