The Experts below are selected from a list of 7596 Experts worldwide ranked by ideXlab platform

Eduardo A B Da Silva - One of the best experts on this subject based on the ideXlab platform.

  • rate Distortion Performance and incremental transmission scheme of compressive sensed measurements in wireless sensor networks
    Sensors, 2019
    Co-Authors: Felipe Henriques, Lisandro Lovisolo, Eduardo A B Da Silva
    Abstract:

    We consider a Wireless Sensor Network (WSN) monitoring environmental data. Compressive Sensing (CS) is explored to reduce the number of coefficients to transmit and consequently save the energy of sensor nodes. Each sensor node collects N samples of environmental data, these are CS coded to transmit M < N values to a sink node. The M CS coefficients are uniformly quantized and entropy coded. We investigate the rate-Distortion Performance of this approach even under CS coefficient losses. The results show the robustness of the CS coding framework against packet loss. We devise a simple strategy to successively approximate/quantize CS coefficients, allowing for an efficient incremental transmission of CS coded data. Tests show that the proposed successive approximation scheme provides rate allocation adaptivity and flexibility with a minimum rate-Distortion Performance penalty.

  • on the empirical rate Distortion Performance of compressive sensing
    International Conference on Image Processing, 2009
    Co-Authors: Adriana Schulz, Luiz Velho, Eduardo A B Da Silva
    Abstract:

    Compressive Sensing (CS) is a new paradigm in signal acquisition and compression that has been attracting the interest of the signal compression community. When it comes to image compression applications, it is relevant to estimate the number of bits required to reach a specific image quality. Although several theoretical results regarding the rate-Distortion Performance of CS have been published recently, there are not many practical image compression results available. The main goal of this paper is to carry out an empirical analysis of the rate-Distortion Performance of CS in image compression. We analyze issues such as the minimization algorithm used and the transform employed, as well as the trade-off between number of measurements and quantization error. From the experimental results obtained we highlight the potential and limitations of CS when compared to traditional image compression methods.

  • ICIP - On the empirical rate-Distortion Performance of Compressive Sensing
    2009 16th IEEE International Conference on Image Processing (ICIP), 2009
    Co-Authors: Adriana Schulz, Luiz Velho, Eduardo A B Da Silva
    Abstract:

    Compressive Sensing (CS) is a new paradigm in signal acquisition and compression that has been attracting the interest of the signal compression community. When it comes to image compression applications, it is relevant to estimate the number of bits required to reach a specific image quality. Although several theoretical results regarding the rate-Distortion Performance of CS have been published recently, there are not many practical image compression results available. The main goal of this paper is to carry out an empirical analysis of the rate-Distortion Performance of CS in image compression. We analyze issues such as the minimization algorithm used and the transform employed, as well as the trade-off between number of measurements and quantization error. From the experimental results obtained we highlight the potential and limitations of CS when compared to traditional image compression methods.

Felipe Henriques - One of the best experts on this subject based on the ideXlab platform.

  • rate Distortion Performance and incremental transmission scheme of compressive sensed measurements in wireless sensor networks
    Sensors, 2019
    Co-Authors: Felipe Henriques, Lisandro Lovisolo, Eduardo A B Da Silva
    Abstract:

    We consider a Wireless Sensor Network (WSN) monitoring environmental data. Compressive Sensing (CS) is explored to reduce the number of coefficients to transmit and consequently save the energy of sensor nodes. Each sensor node collects N samples of environmental data, these are CS coded to transmit M < N values to a sink node. The M CS coefficients are uniformly quantized and entropy coded. We investigate the rate-Distortion Performance of this approach even under CS coefficient losses. The results show the robustness of the CS coding framework against packet loss. We devise a simple strategy to successively approximate/quantize CS coefficients, allowing for an efficient incremental transmission of CS coded data. Tests show that the proposed successive approximation scheme provides rate allocation adaptivity and flexibility with a minimum rate-Distortion Performance penalty.

Vivek K Goyal - One of the best experts on this subject based on the ideXlab platform.

  • on the rate Distortion Performance of compressed sensing
    International Conference on Acoustics Speech and Signal Processing, 2007
    Co-Authors: Alyson K Fletcher, Sundeep Rangan, Vivek K Goyal
    Abstract:

    Encouraging recent results in compressed sensing or compressive sampling suggest that a set of inner products with random measurement vectors forms a good representation of a source vector that is known to be sparse in some fixed basis. With quantization of these inner products, the encoding can be considered universal for sparse signals with known sparsity level. We analyze the operational rate-Distortion Performance of such source coding both with genie-aided knowledge of the sparsity pattern and maximum likelihood estimation of the sparsity pattern. We show that random measurements induce an additive logarithmic rate penalty, i.e., at high rates the Performance with rate R + O(log R) and random measurements is equal to the Performance with rate R and deterministic measurements matched to the source.

  • ICASSP (3) - On the Rate-Distortion Performance of Compressed Sensing
    2007 IEEE International Conference on Acoustics Speech and Signal Processing - ICASSP '07, 2007
    Co-Authors: Alyson K Fletcher, Sundeep Rangan, Vivek K Goyal
    Abstract:

    Encouraging recent results in compressed sensing or compressive sampling suggest that a set of inner products with random measurement vectors forms a good representation of a source vector that is known to be sparse in some fixed basis. With quantization of these inner products, the encoding can be considered universal for sparse signals with known sparsity level. We analyze the operational rate-Distortion Performance of such source coding both with genie-aided knowledge of the sparsity pattern and maximum likelihood estimation of the sparsity pattern. We show that random measurements induce an additive logarithmic rate penalty, i.e., at high rates the Performance with rate R + O(log R) and random measurements is equal to the Performance with rate R and deterministic measurements matched to the source.

Somnath Sengupta - One of the best experts on this subject based on the ideXlab platform.

  • Side information refinement scheme in Transform Domain Distributed Video Coding
    Proceedings of the 2014 IEEE Students' Technology Symposium, 2014
    Co-Authors: Vijay Kumar, Somnath Sengupta
    Abstract:

    In this paper, we propose the Side Information refinement scheme for the Transform Domain Wyner-Ziv video codec. As the quality of Side Information affects the Rate-Distortion Performance of the Wyner-Ziv coding scheme, recent schemes are based on the refinement of the Side Information in the Wyner-Ziv decoding loop to improve the Rate-Distortion Performance. But the schemes are not competitive compared to the low complexity conventional coding schemes(H.264 intra and zero motion codecs) and the conventional H.264 inter coding schemes. As the codec requires huge bit rate to decode the DC band using the initial motion compensated temporal interpolated Side Information, we propose the scheme, by forming the layers with in the DC band and Side Information refinement is applied after decoding coefficients in each layer. The proposed scheme improves the Rate-Distortion Performance and results in the WZ bit rate savings upto 7.78 % compared to the recent schemes.

  • Improving the Rate-Distortion Performance of the Transform Domain Refinement Codec by the Use of Decoder-Driven Adaptive Modes
    2012 IEEE International Conference on Multimedia and Expo Workshops, 2012
    Co-Authors: Vijay Kumar, Somnath Sengupta
    Abstract:

    Distributed video coding (DVC) is an emerging coding paradigm, aiming at low complexity encoders. This paper proposes a new scheme for transform domain Wyner-Ziv (WZ) video codec, where the key and WZ frames are encoded in multiple layers. The layers of the each frame are generated by sub sampling the 4×4 blocks in the spatial domain. After decoding each layer, side information (SI) refinement process is employed to improve the quality of SI to decode the next WZ layer. The codec Performance is further improved by the decoder-driven adaptive skip/WZ control, estimated using the refined SI and the correlation noise. Rate-Distortion Performance of the proposed scheme is tested with several sequences and Performance improvements are noted.

  • ICME Workshops - Improving the Rate-Distortion Performance of the Transform Domain Refinement Codec by the Use of Decoder-Driven Adaptive Modes
    2012 IEEE International Conference on Multimedia and Expo Workshops, 2012
    Co-Authors: Vijay Kumar, Somnath Sengupta
    Abstract:

    Distributed video coding (DVC) is an emerging coding paradigm, aiming at low complexity encoders. This paper proposes a new scheme for transform domain Wyner-Ziv (WZ) video codec, where the key and WZ frames are encoded in multiple layers. The layers of the each frame are generated by sub sampling the 4x4 blocks in the spatial domain. After decoding each layer, side information (SI) refinement process is employed to improve the quality of SI to decode the next WZ layer. The codec Performance is further improved by the decoder-driven adaptive skip/WZ control, estimated using the refined SI and the correlation noise. Rate-Distortion Performance of the proposed scheme is tested with several sequences and Performance improvements are noted.

Gerhard Kramer - One of the best experts on this subject based on the ideXlab platform.

  • rate Distortion Performance of lossy compressed sensing of sparse sources
    IEEE Transactions on Communications, 2018
    Co-Authors: Markus Leinonen, Marian Codreanu, Markku Juntti, Gerhard Kramer
    Abstract:

    We investigate lossy compressed sensing (CS) of a hidden, or remote, source, where a sensor observes a sparse information source indirectly. The compressed noisy measurements are communicated to the decoder for signal reconstruction with the aim to minimize the mean square error Distortion. An analytically tractable lower bound to the remote rate-Distortion function (RDF), i.e., the conditional remote RDF, is derived by providing support side information to the encoder and decoder. For this setup, the best encoder separates into an estimation step and a transmission step. A variant of the Blahut–Arimoto algorithm is developed to numerically approximate the remote RDF. Furthermore, a novel entropy coding based quantized CS method is proposed. Numerical results illustrate the main rate-Distortion characteristics of the lossy CS, and compare the Performance of practical quantized CS methods against the proposed limits.