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Multihypothesis compressed video sensing technique

Azghani, M ; Sharif University of Technology

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  1. Type of Document: Article
  2. DOI: 10.1109/TCSVT.2015.2418586
  3. Abstract:
  4. In this paper, we present a compressive sampling and multihypothesis (MH) reconstruction strategy for video sequences that has a rather simple encoder, while the decoding system is not that complex. We introduce a convex cost function that incorporates the MH technique with the sparsity constraint and the Tikhonov regularization. Consequently, we derive a new iterative algorithm based on these criteria. This algorithm surpasses its counterparts (Elasticnet and Tikhonov) in recovery performance. Besides, it is computationally much faster than Elasticnet and comparable with Tikhonov. Our extensive simulation results confirm these claims
  5. Keywords:
  6. Compressed video sensing ; Muti-hypothesis motion compensation ; Video compression ; Algorithms ; Compressed sensing ; Cost functions ; Image compression ; Iterative methods ; Motion compensation ; Video signal processing ; Compressed video ; Compressive sampling ; Convex cost function ; Extensive simulations ; Iterative algorithm ; Recovery performance ; Sparsity constraints ; Tikhonov regularization ; Motion analysis
  7. Source: IEEE Transactions on Circuits and Systems for Video Technology ; Volume 26, Issue 4 , 2016 , Pages 627-635 ; 10518215 (ISSN)
  8. URL: http://ieeexplore.ieee.org/document/7076640