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video-compression-algorithms
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Reducing motion estimation time with skin detection
, Article 2009 IEEE International Workshop on Imaging Systems and Techniques, IST 2009, Hong Kong, 11 May 2009 through 12 May 2009 ; 2009 , Pages 71-75 ; 9781424434831 (ISBN) ; Shirali Shahreza, S ; IEEE Instrumentation and Measurement Society ; Sharif University of Technology
2009
Abstract
Motion estimation (ME) is one of the key parts of video compression algorithms. But, motion estimation and computation of motion vectors (MVs) are very time con-suming. In this paper, we propose a method for reducing the cost of motion estimation process. During this process, a series of candidate blocks should be searched to find the best motion vectors. In our method, we compare the skin parts of two blocks before comparing all pixel pairs of the two blocks. Having a preprocessing phase, the skin part comparison is performed quickly. This method provides a parameter that can be used to create a balance between the processing time and the motion estimation accuracy. © 2009 IEEE
Multihypothesis compressed video sensing technique
, Article IEEE Transactions on Circuits and Systems for Video Technology ; Volume 26, Issue 4 , 2016 , Pages 627-635 ; 10518215 (ISSN) ; Karimi, M ; Marvasti, F ; Sharif University of Technology
Abstract
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