Abstract
In this paper, we propose a residual reconstruction algorithm based on sub-pixel interpolation for Distributed Compressive Video Sensing (DCVS). First, accurate side information is generated by half pixel interpolating on two neighboring decoded key frames. Then, we reconstruct non-key frame with residual reconstruction algorithm based on block compressive sensing using multi hypothesis predictions. Experimental results reveal that the side information generated by the proposed algorithm is refined as well as the reconstruction quality is increased 0.3–2 dB in PSNR.
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References
Alomari, A.: Distance impact on quality of video streaming services in cloud environments. Int. J. Space Based Situat. Comput. 7(3), 119–128 (2017)
Urakawa, M., Miyazaki, M., Yamada, I., Fujisawa, H., Nakagawa, T.: A study about integrating video contents with web services based on the RDF. Int. J Space-Based Situat. Comput. 6(2), 65–73 (2016)
Girod, B., Aaron, A.M., Rane, S., et al.: Distributed video coding. Proc. IEEE 93(1), 71–83 (2005)
Donoho, D.L.: Compressed sensing. IEEE Trans. Inf. Theory 52(4), 1289–1306 (2006)
Kang, L.W., Lu, C.S.: Distributed compressive video sensing. In: IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2009, pp. 1169–1172. IEEE (2009)
Do, T.T., Chen, Y., Nguyen, D.T., et al.: Distributed compressed video sensing. In: 2009 16th IEEE International Conference on Image Processing (ICIP), pp. 1393–1396. IEEE (2009)
Zhang, X., Wang, A., Zeng, B., et al.: Adaptive distributed compressed video sensing. J. Inf. Hiding Multimed. Signal Process. 5(1), 98–106 (2014)
Liu, Z., Wang, A., Zeng, B., et al.: Distributed compressive video sensing with adaptive measurements based on structural similarity. Chin. J. Electron. 22(3), 594–598 (2013)
Gan, L.: Block compressed sensing of natural images. In: 2007 15th International Conference on Digital Signal Processing, pp. 403–406. IEEE (2007)
Mun, S., Fowler, J.E.: Block compressed sensing of images using directional transforms. In: 2009 16th IEEE International Conference on Image Processing (ICIP), pp. 3021–3024. IEEE (2009)
Fowler, J.E., Mun, S., Tramel, E.W.: Multiscale block compressed sensing with smoothed projected land weber reconstruction. In: 2011 19th European Signal Processing Conference, pp. 564–568. IEEE (2011)
Mun, S., Fowler, J.E.: Residual reconstruction for block-based compressed sensing of video. In: Data Compression Conference (DCC), pp. 183–192. IEEE (2011)
Chen, C., Tramel, E.W., Fowler, J.E.: Compressed-sensing recovery of images and video using multi hypothesis predictions. In: 2011 Conference Record of the Forty Fifth Asilomar Conference on Signals, Systems and Computers (ASILOMAR), pp. 1193–1198. IEEE (2011)
Acknowledgement
This work is financially supported by NSFC (61703201), NSF of Jiangsu Province (BK20170765), and Key research and development plan of Jiangsu Province (SBE2017741114).
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Chen, R., Tong, Y., Yang, J., Wu, M. (2019). Residual Reconstruction Algorithm Based on Sub-pixel Multi-hypothesis Prediction for Distributed Compressive Video Sensing. In: Barolli, L., Javaid, N., Ikeda, M., Takizawa, M. (eds) Complex, Intelligent, and Software Intensive Systems. CISIS 2018. Advances in Intelligent Systems and Computing, vol 772. Springer, Cham. https://doi.org/10.1007/978-3-319-93659-8_54
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DOI: https://doi.org/10.1007/978-3-319-93659-8_54
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