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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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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