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Perceptual-Based Distributed Compressed Video Sensing
2015 Data Compression Conference, 2015This paper proposes an approach of compressed sensing (CS) of video in which distributed video coding DVC and CS are integrated as in [1], and the sensing matrix is modulated in suit of [2] but with proposed fixed weighting strategy to certain DCT coefficients in an effort to improve the visual quality of reconstruction.
Sawsan Abdellatif Abdelsalam Elsayed +1 more
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Perceptually-aware distributed compressive video sensing
2015 Visual Communications and Image Processing (VCIP), 2015By combining the advantages of distributed video coding (DVC) and compressive sensing (CS), distributed compressive video sensing (DCVS) poses itself as a very promising low-complexity video coding framework for distributed applications. In order to improve the rate-distortion performance of DCVS, much research efforts have been focused on exploring ...
Jin Xu 0005 +3 more
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Noncoherent compressive sensing with application to distributed radar
2011 45th Annual Conference on Information Sciences and Systems, 2011We consider a multi-static radar scenario with spatially dislocated receivers that can individually extract delay information only. Furthermore, we assume that the receivers are not phase-synchronized, so the measurements across receivers can only be combined noncoherently.
Christian R. Berger, José M. F. Moura
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Distributed Compressive Sensing for Correlated Information Sources
2017The abstract should summarize the contents of the paper and should Distributed Compressive Sensing (DCS) improves the signal recovery performance of multi signal ensembles by exploiting both intra- and inter-signal correlation and sparsity structure. In this paper, we propose a novel algorithm, which improves detection performance even without a priori-
Jeong-Hun Park +5 more
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A joint recovery algorithm for distributed compressed sensing
Transactions on Emerging Telecommunications Technologies, 2012ABSTRACTDistributed compressed sensing exploits the correlation among multiple signals to reduce the number of measurements required for recovery. In this paper, we propose a recovery algorithm for a type of joint sparsity model, where all signals share a common sparse component and each individual signal contains a sparse innovation component.
Wenbo Xu 0003 +3 more
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Distributed Outlier Detection using Compressive Sensing
Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data, 2015Computing outliers and related statistical aggregation functions from large-scale big data sources is a critical operation in many cloud computing scenarios, e.g. service quality assurance, fraud detection, or novelty discovery. Such problems commonly have to be solved in a distributed environment where each node only has a local slice of the entirety ...
Ying Yan 0006 +6 more
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A robust and efficient algorithm for distributed compressed sensing
Computers & Electrical Engineering, 2011zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Qun Wang, Zhiwen Liu
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Distributed Video Coding Based on Compressed Sensing
2012 IEEE International Conference on Multimedia and Expo Workshops, 2012Compressed Sensing (CS) is a new approach to signal acquisition that can potentially allow us to design very simple video encoders that can be implemented on mobile devices with limited resources. However, previously proposed CS based video codec either require a conventional video codec or a feedback channel for effective operation, thus increasing ...
Yousuf Baig +2 more
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Compressive sensing in distributed applications
2010The theory of compressive sensing (CS) has recently been proposed as a framework for joint signal acquisition and compression by replacing the standard sample by sample measurement approach with the idea of collecting a set of random projections of the signal; it has already been successfully employed in a number of signal processing applications, e.g.,
GAETA, Rossano +2 more
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A novel subspace pursuit of residual correlation step algorithm for distributed compressed sensing
Signal Processing, 2023Man Zhao, Tailin Han, Mingchi Ju
exaly