Results 231 to 240 of about 83,836 (252)

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
Zhu, Shuyuan, Zeng, Bing, Gabbouj, Moncef   +2 more
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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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Sampled speech compression system

The Journal of the Acoustical Society of America, 1985
A sampled speech compression and expansion system, for two-dimensional prssing of speech or other type of audio signal, comprises transmit/encode apparatus and receive/decode apparatus. The transmit/encode apparatus comprises a low-pass filter, adapted to receive an input signal, for passing through low-frequency analog signals.
James M. Alsup, Harper J. Whitehouse
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Wideband Photonic Compressive Sampling

Optical Fiber Communication Conference, 2012
We discuss the potential benefits of combining the high speeds, broad modulation bandwidths and precision timing of photonic sampling techniques with recent advances in the theory and algorithms of compressive sampling.
Thomas R. Clark, Keir C. Lauritzen, Matthew D. Sharp, Dennis G. Lucarelli, Patrick T. Callahan, Michael L. Dennis   +5 more
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Sub-sampled IFFT based compressive sampling

TENCON 2015 - 2015 IEEE Region 10 Conference, 2015
In this paper, a new approach based on Sub-sampled Inverse Fast Fourier Transform (SSIFFT) for efficiently acquiring compressive measurements is proposed, which is motivated by random filter based method and sub-sampled FFT. In our approach, to start with, we multiply the FFT of input signal and that of random-tap FIR filter in frequency domain and ...
null Liang Zhongyin, null Huang Jianjun, null Huang Jingxiong   +2 more
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Compressed sampling for heart rate monitoring

Computer Methods and Programs in Biomedicine, 2012
For the first time compressed sampling (CS) has been applied to heart rate (HR) measurements. The signals can be reconstructed from samples far below the Nyquist rate with negligible small errors, a sampling reduction of 8 has been demonstrated in the paper. As a result, the bitrate of the CS sampler is half when compared to a normal sampler.
Oliver, Faust, U Rajendra, Acharya, Jianguo, Ma, Lim Choo, Min, Toshiyo, Tamura   +4 more
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Compressive quantization versus compressive sampling in image digitization

2012 IEEE Aerospace Conference, 2012
Digital image compression reduces the bandwidth, time, and energy needed for transmission of images and signals, as well as memory needed for their storage. However, it cannot solve the digitization problems. Recently proposed compressive sampling (or sensing) solves these problems by reducing the average number of projections required for representing
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Compressive Sampling with Coefficients Random Permutations for Image Compression

2011 International Conference on Multimedia and Signal Processing, 2011
The different image block has different sparsity or compressibility in transform domain; in general, the blocks in smooth region have stronger sparsity while those in texture or edge region have weaker sparsity. Based on this observation, a novel block DCT based sampling scheme with coefficients random permutations for image compressive sensing has ...
Zhirong Gao, Chengyi Xiong, Cheng Zhou, Hanxin Wang   +3 more
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Measurement Compression in Compressive Sampling Based Distributed Video Coding

2010 2nd International Conference on Information Engineering and Computer Science, 2010
Compressive sampling (CS) theory and distributed video coding (DVC) are two techniques suitable to scenarios where a video codec with simple encoder and complex decoder is desired. The combination of CS theory and DVC is a new research trend in this field and several integrated schemes have now appeared.
Xiaoran Hao, Bojin Zhuang, Anni Cai
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Recurrent networks for compressive sampling

Neurocomputing, 2014
This paper develops two neural network models, based on Lagrange programming neural networks (LPNNs), for recovering sparse signals in compressive sampling. The first model is for the standard recovery of sparse signals. The second one is for the recovery of sparse signals from noisy observations.
Chi-Sing Leung, John Sum, A.G. Constantinides   +2 more
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