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检索条件"主题词=joint source-channel coding"

共 619 条记录，以下是561-570 订阅

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Transmitting k samples over the Gaussian channel: energy-distortion tradeoff

Transmitting <i>k</i> samples over the Gaussian channel: ene...

引用

IEEE Information Theory Workshop (ITW)

作者： Kostina, Victoria Polyanskiy, Yury Verdu, Sergio CALTECH Pasadena CA 91125 USA MIT Cambridge MA 02139 USA Princeton Univ Princeton NJ 08544 USA

ISBN: (纸本)9781479955268

We investigate the minimum transmitted energy required to reproduce k source samples with a given fidelity after transmission over a memoryless Gaussian channel. In particular, we analyze the reduction in transmitted energy that accrues thanks to the availability of noiseless feedback. Allowing a nonvanishing excess distortion probability epsilon boosts the asymptotic fundamental limit by a factor of 1 - epsilon, with or without feedback. If feedback is available, achieving guaranteed distortion with finite average energy is possible.

关键词： joint source-channel coding lossy compression single-shot method finite-blocklength regime rate-distortion theory feedback memoryless channels Gaussian channels energy-distortion tradeoff Shannon theory

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Compressed Sensing Based Video Multicast

Compressed Sensing Based Video Multicast

引用

Conference on Visual Communications and Image Processing (VCIP)

作者： Schenkel, Markus B. Luo, Chong Frossard, Pascal Wu, Feng Microsoft Res Asia Beijing Peoples R China Ecole Polytech Fed Lausanne Signal Proc Lab LTS4 Lausanne Switzerland

ISBN: (纸本)9780819482341

We propose a new scheme for wireless video multicast based on compressed sensing. It has the property of graceful degradation and, unlike systems adhering to traditional separate coding, it does not suffer from a cliff effect. Compressed sensing is applied to generate measurements of equal importance from a video such that a receiver with a better channel will naturally have more information at hands to reconstruct the content without penalizing others. We experimentally compare different random matrices at the encoder side in terms of their performance for video transmission. We further investigate how properties of natural images can be exploited to improve the reconstruction performance by transmitting a small amount of side information. And we propose a way of exploiting inter-frame correlation by extending only the decoder. Finally we compare our results with a different scheme targeting the same problem with simulations and find competitive results for some channel configurations.

关键词： Compressed Sensing Video Multicast joint source-channel coding

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SoftCast: One-Size-Fits-All Wireless Video 10

SoftCast: One-Size-Fits-All Wireless Video

引用

ACM SIGCOMM Conference 2010

作者： Jakubczak, Szymon Katabi, Dina MIT Cambridge MA 02139 USA

ISBN: (纸本)9781450302012

The focus of this demonstration is the performance of streaming video over the mobile wireless channel. We compare two schemes: the standard approach to video which transmits H. 264/AVC-encoded stream over 802.11-like PHY, and SoftCast - a clean-slate design for wireless video where the source transmits one video stream that each receiver decodes to a video quality commensurate with its specific instantaneous channel quality.

关键词： Algorithms Design Performance Theory wireless networks scalable video communications joint source-channel coding

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Forward error protection of H.263-DG bit streams

Forward error protection of H.263-DG bit streams

引用

11th IEEE Mediterranean Electrotechnical Conference (IEEE MELECON 2002)

作者： Tavares, J Navarro, A Univ Aveiro Inst Telecommun P-3810 Aveiro Portugal

ISBN: (纸本)0780375270

The purpose of our work is to report a solution for the problem of the joint source-channel coding (JSCC) of the H.263-DG bit stream to be transmitted through error prone channels. Our investigations show that the H.263 syntactical elements have different error sensitivities. This result led us to classify them into several classes of different significance. A more robust scheme, named H.263-DG, was obtained by optimally grouping the H.263 classes. This resulted in a maximum PSNR gain of 5.5 dB at 10(-3) bit error rate (BER). The solution presented in this paper further increases the error robustness by unequal protecting the classes of H.263-DG coding scheme. A good reconstructed video quality is achieved at 64 Kbps transmission rate using Rate Compatible Punctured Convolutional (RCPC) codes. By exploiting the different class sensitivities, we present an Unequal Error Protection (UEP) scheme that outperforms the optimal Equal Error Protection (EEP). The forward error correction adopted has resulted in PSNR improvements over 20 dB for bit error rates higher than 4x10(-3).

关键词： H.263 error sensitivity analysis data grouping forward error protection joint source-channel coding

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To code or not to code: Revisited

To code or not to code: Revisited

引用

IEEE Information Theory Workshop (ITW)

作者： Kostina, Victoria Verdu, Sergio Princeton Univ Dept Elect Engn Princeton NJ 08544 USA

ISBN: (纸本)9781467302234;9781467302241

We revisit the dilemma of whether one should or should not code when operating under delay constraints. In those curious cases when the source and the channel are probabilistically matched so that symbol-by-symbol coding is optimal in terms of the average distortion achieved, we show that it also achieves the dispersion of joint source-channel coding. Moreover, even in the absence of such probabilistic matching between the source and the channel, symbol-by-symbol transmission, though asymptotically suboptimal, might outperform not only separate source-channel coding but also the best known random-coding joint source-channel coding achievability bound in the finite blocklength regime.

关键词： Achievability converse finite blocklength regime joint source-channel coding lossy source coding memoryless sources rate-distortion theory Shannon theory

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Adaptive Early Exiting for Collaborative Inference over Noisy Wireless channels 1

Adaptive Early Exiting for Collaborative Inference over Nois...

引用

1st IEEE International Conference on Machine Learning for Communication and Networking (IEEE ICMLCN)

作者： Jankowski, Mikolaj Gunduz, Deniz Mikolajczyk, Krystian Imperial Coll London London England Boeing Poland Gdansk Poland

ISBN: (纸本)9798350343205;9798350343199

Collaborative inference systems are one of the emerging solutions for deploying deep neural networks (DNNs) at the wireless network edge. Their main idea is to divide a DNN into two parts, where the first is shallow enough to be reliably executed at edge devices of limited computational power, while the second part is executed at an edge server with higher computational capabilities. The main advantage of such systems is that the input of the DNN gets compressed as the subsequent layers of the shallow part extract only the information necessary for the task. As a result, significant communication savings can be achieved compared to transmitting raw input samples. In this work, we study early exiting in the context of collaborative inference, which allows obtaining inference results at the edge device for certain samples, without the need to transmit the partially processed data to the edge server at all, leading to further communication savings. The central part of our system is the transmission-decision (TD) mechanism, which, given the information from the early exit, and the wireless channel conditions, decides whether to keep the early exit prediction or transmit the data to the edge server for further processing. In this paper, we evaluate various TD mechanisms and show experimentally, that for an image classification task over the wireless edge, proper utilization of early exits can provide both performance gains and significant communication savings.

关键词： Collaborative learning edge machine learning joint source-channel coding early exit

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DCS-JSCC: Leveraging Deep Compressed Sensing into JSCC for Wireless Image Transmission 25

DCS-JSCC: Leveraging Deep Compressed Sensing into JSCC for W...

引用

25th IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)

作者： Jarrahi, Mohammad Amin Bourtsoulatze, Eirina Abolghasemi, Vahid Univ Essex Sch Comp Sci & Elect Engn CSEE Colchester Essex England

ISBN: (纸本)9798350393194;9798350393187

This paper presents a novel approach integrating deep compressed sensing (DCS) into joint source-channel coding (JSCC) for efficient image transmission. Leveraging the capabilities of DCS, the proposed method offers enhanced compression and resilience to channel noise in wireless image transmission systems. A key component of the proposed method is utilising a convolutional neural network (CNN) structure to implement a block-based DCS technique for image compression. The proposed encoder consists of a well-designed CNN-based structure to capture structural information of the input image which is subsequently mapped to a complex-value domain. Finally, the proposed decoder deals with channel noise and reconstructs the original image. Applying the proposed CNN-based sampling matrices and reconstruction capabilities helps the proposed algorithm enhance image compression and reconstruction in wireless transmission systems. The CIFAR-10 and Kodak datasets are used to evaluate the performance of DCS-JSCC, showing a significant improvement in terms of Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM), across different channel Signal-to-Noise Ratios (SNRs) and channel bandwidth values in comparison with state-of-art JSCC frameworks. Experimental evaluations demonstrate the effectiveness of the proposed method in achieving superior compression levels and maintaining image quality under varying channel conditions.

关键词： Wireless image transmission joint source-channel coding compressed sensing deep learning

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Robust video transmission using adaptive bit allocation

Robust video transmission using adaptive bit allocation

引用

Conference on Visual Communications and Image Processing 2002

作者： Kung, WY Kim, CS Ku, R Kuo, CCJ Univ So Calif Integrated Media Syst Ctr Los Angeles CA 90089 USA

ISBN: (纸本)0819444111

A jointly designed source-channel coding technique that protects video signals against transmission errors is proposed in this work. We develop a low-complexity source model which can estimate the bitrate and the quantization error for each packet. Also, the channel distortion is estimated based on the packet importance and the packet loss rate. The packet importance is measured as the mean square error between the error-free reconstruction and the concealed reconstruction, and the packet loss rate is determined by channel conditions. According to the informed channel condition, the encoder adaptively assigns the quantization parameter and the channel code rate to each packet so that the expected mean square error due to source and channel distortions is minimized subject to a constraint on the overall bitrate. Simulation results show that the proposed adaptive system provides acceptable image quality even in a high bit error rate environment.

关键词： wireless video transmission QP adaptation channel code assignment joint source-channel coding

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A Perceptual Study of the Decoding Process of the SoftCast Wireless Video Broadcast Scheme 23

A Perceptual Study of the Decoding Process of the SoftCast W...

引用

23rd IEEE International Workshop on Multimedia Signal Processing (IEEE MMSP)

作者： Trioux, Anthony Valenzise, Giuseppe Cagnazzo, Marco Kieffer, Michel Coudoux, Francois-Xavier Corlay, Patrick Gharbi, Mohamed Univ Lille Univ Polytech Hauts de France CNRS YNCREACent LilleUMR 8520IEMNDOAE F-59313 Valenciennes France Inst Polytech Paris Telecom ParisTech LTCI Paris France Univ Paris Saclay CNRS Cent Supelec L2S F-91192 Gif Sur Yvette France

ISBN: (纸本)9781665432870

The SoftCast scheme has been proposed as a promising alternative to traditional video broadcasting systems in wireless environments. In its current form, SoftCast performs image decoding at the receiver side by using a Linear Least Square Error (LLSE) estimator. Such approach maximizes the reconstructed quality in terms of Peak Signal-to-Noise Ratio (PSNR). However, we show that the LLSE induces an annoying blur effect at low channel Signal-to-Noise Ratio (CSNR) quality. To cancel this artifact, we propose to replace the LLSE estimator by the Zero-Forcing (ZF) one. In order to better understand the perceived quality offered by these two estimators, a mathematical characterization as well as an objective and subjective studies are performed. Results show that the gains brought by the LLSE estimator, in terms of PSNR and Structural SIMiliraty (SSIM), are limited and quickly tend to null value as the CSNR increases. However, higher gains are obtained by the ZF estimator when considering the recent Video Multi-method Assessment Fusion (VMAF) metric proposed by Netflix, which evaluates the perceptual video quality. This result is confirmed by the subjective assessment.

关键词： SoftCast Linear Video coding Quality assessment Visual artifacts joint source-channel coding

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Deep FAVIB: Deep Learning-Based Forward-Aware Quantization via Information Bottleneck Method 59

Deep FAVIB: Deep Learning-Based Forward-Aware Quantization v...

引用

59th Annual IEEE International Conference on Communications (IEEE ICC)

作者： Hummert, Matthias Hassanpour, Shayan Wuebben, Dirk Dekorsy, Armin Univ Bremen Dept Commun Engn D-28359 Bremen Germany

ISBN: (纸本)9781728190549

We focus on a (generic) joint source-channel coding problem, appearing in a broad variety of real-world application. Explicitly, a noisy observation from a user/source signal should be compressed, ahead of getting forwarded over an error-prone and rate-limited channel to a remote processing unit. The design problem shall be formulated in a fashion that the impacts of the forward link are taken into account. Aligned with the Information Bottleneck (IB) method, we consider the Mutual Information (MI) as the fidelity criterion, and work out a data-driven approach to tackle the underlying design problem based upon a finite sample set. For that, we derive a tractable variational lower-bound of the objective functional, and present a general learning architecture which can be used to optimize the given lower-bound by standard training of the encoder and decoder Deep Neural Networks. This approach that is, principally, based upon the (generative) latent variable models, extends the concepts of Variational AutoEncoder (VAE) and Deep Variational Information Bottleneck (Deep VIB) for (remote) source coding to the context of joint source-channel coding. We validate the effectiveness of our approach by several numerical simulations over typical transmission scenarios.

关键词： Information bottleneck variational autoencoder deep learning quantization joint source-channel coding 6G

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