Abstract
In this paper, the application of compressive sensing theory for digital video has been reviewed and analyzed. The compressive sensing (CS) is a new signal processing theory which overcomes the limitation of Shannon–Nyquist sampling theorem. The compressive sensing exploits the redundancy within the signal to get samples of the signal at sub-Nyquist rates. The signal can be reconstructed from these samples when it is fed to CS recovery algorithm. Here challenges and various approaches to CS theory for digital video are discussed, which motivate the future research. The comparative evaluation of CS theory for color digital video using different transform basis is also discussed and analyzed in this paper.
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Candes, E.: Compressive sampling. In: Proceedings of the International Congress of Mathematicians, Madrid, Spain, pp. 1433–1452, June 2006
Donoho, D.: Compressed sensing. IEEE Trans. Inf. Theory 52(4), 1289–1306 (2006)
Baraniuk, R.: Lecture notes “Compressive Sensing”. IEEE Signal Process. Mag. 24(4), 118–124 (2007)
Baraniuk, R., Goldstein, T., Sankaranarayanan, A., Studer, C., Veeraraghavan, A., Wakin, M.: Compressive video sensing. IEEE Signal Process. Mag. 52–66 (2017)
Wakin, M., Laska, J., Duarte, M., Baron, D., Sarvotham, S., Takhar, D., Kelly, K., Baraniuk, R.: Compressive imaging for video representation and coding. In: Picture Coding Symposium, vol. 1, no. 13 (2006)
Park, J., Wakin, M.: A multiscale framework for compressive sensing of video. In: Picture Coding Symposium, pp. 1–4 (2009)
Stankovic, V., Stankovic, L., Cheng, S.: Compressive video sampling. In: 16th IEEE European Signal Processing Conference, pp. 1–5 (2008)
Fowler, J.: Block-Based Compressed Sensing of Images and Video, Mississippi State University, Mar 2010
Yang, J., Yuan, X., Liao, X., Llull, P., Brady, D., Sapiro, G., Carin, L.: Video compressive sensing using Gaussian mixture models. IEEE Trans. Image Process. 23(11), 4863–4878 (2014)
Tropp, J., Gilbert, A.: Signal recovery from random measurements via orthogonal matching pursuit. IEEE Trans. Inf. Theory 53(12), 4655–4666 (2007)
Needell, D., Tropp, J.: CoSaMP: iterative signal recovery from incomplete and inaccurate samples. Appl. Comput. Harmon. Anal. 26(3), 301–321 (2009)
Wei, D., Milenkovic, O.: Subspace pursuit for compressive sensing signal reconstruction. IEEE Trans. Inf. Theory 55(5), 2230–2249 (2009)
Mark, M., Grgic, S., Grgic, M.: Picture quality measures in image compression systems. In: EUROCON 2003, Ljubljana, Slovenia, 233-2-7 (2003)
Wang, Z., Bovik, A.: A universal image quality index. J. IEEE Signal Process. Lett. 9(3), 84–88 (2004)
Jain, A.: Fundamental of Digital Image Processing, pp. 150–153. Prentice Hall Inc., New Jersey (1999)
Yan, J.: Wavelet Matrix, Department of Electrical and Computer Engineering, University of Victoria, Victoria, BC, Canada (2009)
Vidakovic, B.: Statistical Modelling by Wavelets, pp. 115–116. Wiley (1999)
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Thanki, R., Borisagar, K., Dwivedi, V. (2018). Review and Comparative Evaluation of Compressive Sensing for Digital Video. In: Sa, P., Bakshi, S., Hatzilygeroudis, I., Sahoo, M. (eds) Recent Findings in Intelligent Computing Techniques . Advances in Intelligent Systems and Computing, vol 709. Springer, Singapore. https://doi.org/10.1007/978-981-10-8633-5_42
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DOI: https://doi.org/10.1007/978-981-10-8633-5_42
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