Results 251 to 260 of about 2,561,650 (296)

Compressive Sampling and Lossy Compression

IEEE Signal Processing Magazine, 2008
Recent results in compressive sampling have shown that sparse signals can be recovered from a small number of random measurements. This property raises the question of whether random measurements can provide an efficient representation of sparse signals in an information-theoretic sense.
Vivek K. Goyal, Alyson K. Fletcher, Sundeep Rangan   +2 more
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Reweighted Compressive Sampling for image compression

2009 Picture Coding Symposium, 2009
Compressive Sampling (CS), is an emerging theory which points us a promising direction of designing novel efficient data compression techniques. However, the conventional CS adopts a non-discriminated sampling scheme which usually gives poor performance on realistic complex signals.
Yi Yang 0041, Oscar C. Au, Lu Fang 0001, Xing Wen, Weiran Tang   +4 more
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Compressive covariance sampling

2013 Information Theory and Applications Workshop (ITA), 2013
Most research efforts in the field of compressed sensing have been pointed towards analyzing sampling and reconstruction techniques for sparse signals, where sampling rates below the Nyquist rate can be reached. When only second-order statistics or, equivalently, covariance information is of interest, perfect signal reconstruction is not required and ...
Daniel Romero 0004, Geert Leus
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Variable Density Compressed Image Sampling [PDF]

IEEE Transactions on Image Processing, 2010
Compressed sensing (CS) provides an efficient way to acquire and reconstruct natural images from a limited number of linear projection measurements leading to sub-Nyquist sampling rates. A key to the success of CS is the design of the measurement ensemble.
Gonzalo R. Arce, Zhongmin Wang, G R Arce
exaly   +4 more sources

Adaptive sampling for compressed sensing based image compression

2014 IEEE International Conference on Multimedia and Expo (ICME), 2014
A jointly-reweighted block-based compressed sensing scheme.A generic measurement allocation algorithm to assign CS-measurements.Statistical parameters as allocation factors.Two solutions to implement the adaptive measurement allocation.Remarkable quality improvement over the traditional reweighted BCS scheme. The compressed sensing (CS) theory has been
Shuyuan Zhu, Bing Zeng 0001, Moncef Gabbouj   +2 more
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Compressive Sampling for Signal Classification

2006 Fortieth Asilomar Conference on Signals, Systems and Computers, 2006
Compressive sampling (CS), also called compressed sensing, entails making observations of an unknown signal by projecting it onto random vectors. Recent theoretical results show that if the signal is sparse (or nearly sparse) in some basis, then with high probability such observations essentially encode the salient information in the signal.
Haupt, J., Castro, R.M., Nowak, R., Fudge, G., Yeh, A.   +4 more
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Universal algorithm for compressive sampling

2015 23rd European Signal Processing Conference (EUSIPCO), 2015
In a standard compressive sampling (CS) setup, we develop a universal algorithm where multiple CS reconstruction algorithms participate and their outputs are fused to achieve a better reconstruction performance. The new method is called universal algorithm for CS (UACS) that is iterative in nature and has a restricted isometry property (RIP) based ...
Ahmed Zaki, Saikat Chatterjee, Lars K. Rasmussen   +2 more
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Watermarking for Compressive Sampling Applications

2012 Eighth International Conference on Intelligent Information Hiding and Multimedia Signal Processing, 2012
Compressive sampling is a newly developed topic in the field of data compression. For current researches, compressive sampling techniques focus on compression performances. There are very few papers aiming at the integration of watermarking into compressive sampling systems.
Hsiang-Cheh Huang, Feng-Cheng Chang, Chun-Hsien Wu, Wei-Hao Lai   +3 more
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Compressive sampling in array processing

2013 5th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), 2013
In this paper, we propose a sampling architecture for the efficient acquisition of multiple signals lying in a subspace. We show that without the knowledge of the signal subspace, the proposed sampling architecture acquires the signals at a sub-Nyquist rate.
Ali Ahmed 0004, Justin K. Romberg
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Compressive sampling hardware reconstruction

Proceedings of 2010 IEEE International Symposium on Circuits and Systems, 2010
Compressive Sampling reconstruction techniques require computationally intensive algorithms, often using L1 optimization to reconstruct a signal that was originally sampled at a sub-Nyquist rate. In this work we present a VLSI implementation of a computationally efficient algorithm named Orthogonal Matching Pursuit. We further optimize the algorithm to
Avi Septimus, Raphael Steinberg
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