Model-based quantization for perceptually weighted compressed video sensing
Author:
Affiliation:
1. School of Electronics, Communications, and Computing, Egypt-Japan University of Science and Technology
2. Kyushu University
Publisher
Institute of Electronics, Information and Communications Engineers (IEICE)
Subject
General Medicine
Link
https://www.jstage.jst.go.jp/article/comex/5/7/5_2016XBL0071/_pdf
Reference9 articles.
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2. [2] K. Hayashi, M. Nagahara, and T. Tanaka, “A user's guide to compressed sensing for communications systems,” IEICE Trans. on Communications, vol. E96-B, no. 3, pp. 685–712, Mar. 2013. DOI:10.1587/transcom.E96.B.685
3. [3] J. N. Laska and R. G. Baraniuk, “Regime change: bit-depth versus measurement-rate in compressive sensing,” IEEE Trans. Signal Process., vol. 60, no. 7, pp. 3496–3505, 2012. DOI:10.1109/TSP.2012.2194710
4. [4] H. Liu, B. Song, F. Tian, and H. Qin, “Joint sampling rate and bit-depth optimization in compressive video sampling,” IEEE Trans. Multimed., vol. 16, no. 6, pp. 1549–1562, 2014. DOI:10.1109/TMM.2014.2328324
5. [5] S. Elsayed, M. Elsabrouty, O. Muta, and H. Furukawa, “Perceptual-based compressed video sensing,” IEICE Commun. Express, vol. 4, no. 8, pp. 251–257, Aug. 2015. DOI:10.1587/comex.4.251
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