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Enhancing distributed source coding With Encoder-Centric Frequency Adaptation and Spatial Transformation

IEEE TRANSACTIONS ON MULTIMEDIA 2025年 27卷 2582-2592页

作者： Xu, Hao Tan, Bin Chen, Yihao Hu, Die Wu, Jun Tongji Univ Coll Elect & Informat Engn Shanghai 201804 Peoples R China Jinggangshan Univ Coll Elect & Informat Engn Jian 343009 Peoples R China Fudan Univ Sch Commun Sci & Engn Shanghai 200433 Peoples R China Fudan Univ Sch Comp Sci Shanghai 200433 Peoples R China Fudan Univ Shanghai Key Lab Intelligent Informat Proc Shanghai 200433 Peoples R China

Current methodologies in distributed source coding have predominantly investigated decoder-focused strategies, emphasizing the alignment and exploitation of side information. This study introduces a paradigm shift by presenting an encoder-centric algorithm that conducts proactive optimization in the frequency domain. This shift is motivated by the current deep learning models' tendency to passively extract high-frequency elements, such as contours and content in the spatial domain at the encoder side, without considering the frequency characteristics of these spatial components. Unlike current trends, the proposed scheme actively selects the essential frequency components directly in the frequency domain by introducing an adaptive self-learning filter, enabling the encoder to discern and retain critical frequency components effectively and precisely. Furthermore, we align the side information in the spatial domain before feature extraction and implement an affine transformation-based alignment strategy to utilize the side information better. By leveraging the shared frequency domain components of the image pairs, the proposed algorithm adeptly learns affine coefficients to accomplish precise spatial alignment. This dual strategy of proactive encoder optimization and decoder alignment via affine transformations is highly efficient, outperforming existing state-of-the-art methods in distributed source coding when tested across two diverse datasets by an average of 0.5 dB in PSNR.

关键词： Frequency-domain analysis Image coding Feature extraction Decoding Image reconstruction source coding Optimization Correlation Information filters Transformers distributed source coding frequency-domain filtering affine transformation

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Neural distributed source coding

IEEE JOURNAL ON SELECTED AREAS IN INFORMATION THEORY

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IEEE JOURNAL ON SELECTED AREAS IN INFORMATION THEORY 2024年 5卷 493-508页

作者： Whang, Jay Nagle, Alliot Acharya, Anish Kim, Hyeji Dimakis, Alexandros G. Gemini Team Google DeepMind Mountain View CA 94043 USA Univ Texas Austin ECE Dept Austin TX USA

We consider the distributed source coding (DSC) problem concerning the task of encoding an input in the absence of correlated side information that is only available to the decoder. Remarkably, Slepian and Wolf showed in 1973 that an encoder without access to the side information can asymptotically achieve the same compression rate as when the side information is available to it. This seminal result was later extended to lossy compression of distributed sources by Wyner, Ziv, Berger, and Tung. While there is vast prior work on this topic, practical DSC has been limited to synthetic datasets and specific correlation structures. Here we present a framework for lossy DSC that is agnostic to the correlation structure and can scale to high dimensions. Rather than relying on hand-crafted source modeling, our method utilizes a conditional Vector-Quantized Variational auto-encoder (VQ-VAE) to learn the distributed encoder and decoder. We evaluate our method on multiple datasets and show that our method can handle complex correlations and achieves state-of-the-art PSNR.

关键词： distributed source coding Berger-Tung inner bound Slepian-Wolf coding Wynzer-Ziv coding vector-quantized variational auto-encoder

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Information-Theoretic Tools to Understand distributed source coding in Neuroscience

IEEE JOURNAL ON SELECTED AREAS IN INFORMATION THEORY

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IEEE JOURNAL ON SELECTED AREAS IN INFORMATION THEORY 2024年 5卷 509-519页

作者： Feldman, Ariel K. Venkatesh, Praveen Weber, Douglas J. Grover, Pulkit Carnegie Mellon Univ Neurosci Inst Pittsburgh PA 15213 USA Allen Inst Seattle WA 98109 USA Univ Washington Dept Physiol & Biophys Seattle WA 98109 USA Carnegie Mellon Univ Mech Engn Dept & Neurosci Inst Pittsburgh PA 15213 USA Carnegie Mellon Univ Elect & Comp Engn Dept & Neurosci Inst Pittsburgh PA 15213 USA

This paper brings together topics of two of Berger's main contributions to information theory: distributed source coding, and living information theory. Our goal is to understand which information theory techniques can be helpful in understanding a distributed source coding strategy used by the natural world. Towards this goal, we study the example of the encoding of location of an animal by grid cells in its brain. We use information measures of partial information decomposition (PID) to assess the unique, redundant, and synergistic information carried by multiple grid cells, first for simulated grid cells utilizing known encodings, and subsequently for data from real grid cells. In all cases, we make simplifying assumptions so we can assess the consistency of specific PID definitions with intuition. Our results suggest that the measure of PID proposed by Bertschinger et al. (Entropy, 2014) provides intuitive insights on distributed source coding by grid cells, and can be used for subsequent studies for understanding grid-cell encoding as well as broadly in neuroscience.

关键词： distributed source coding grid cells neuroscience partial information decomposition

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Federated Learning With Lossy distributed source coding: Analysis and Optimization

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IEEE TRANSACTIONS ON COMMUNICATIONS 2023年第8期71卷 4561-4576页

作者： Yang, Huiyuan Ding, Tian Yuan, Xiaojun Univ Elect Sci & Technol China Natl Key Lab Wireless Commun Chengdu 611731 Peoples R China Chinese Univ Hong Kong Dept Informat Engn Hong Kong Peoples R China

Recently, federated learning (FL), which replaces data sharing with model sharing, has emerged as an efficient and privacy-friendly machine learning (ML) paradigm. One of the main challenges in FL is the huge communication cost for model aggregation. Many compression/quantization schemes have been proposed to reduce the communication cost for model aggregation. However, the following question remains unanswered: What is the fundamental trade-off between the communication cost and the FL convergence performance? In this paper, we manage to answer this question. Specifically, we first put forth a general framework for model aggregation performance analysis based on the rate-distortion theory. Under the proposed analysis framework, we derive an inner bound of the rate-distortion region of model aggregation. We then conduct an FL convergence analysis to connect the aggregation distortion and the FL convergence performance. We formulate an aggregation distortion minimization problem to improve the FL convergence performance. Two algorithms are developed to solve the above problem. Numerical results on aggregation distortion, convergence performance, and communication cost demonstrate that the baseline model aggregation schemes still have great potential for further improvement.

关键词： Federated learning model aggregation rate-distortion theory distributed source coding Berger-Tung coding majorization-minimization

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A Low-Complexity and Adaptive distributed source coding Design for Model Aggregation in distributed Learning

IEEE OPEN JOURNAL OF THE COMMUNICATIONS SOCIETY

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IEEE OPEN JOURNAL OF THE COMMUNICATIONS SOCIETY 2022年 3卷 2444-2460页

作者： Zhang, Naifu Tao, Meixia Shanghai Jiao Tong Univ Dept Elect Engn Shanghai 200240 Peoples R China

A major bottleneck in distributed learning is the communication overhead of exchanging intermediate model update parameters between the worker nodes and the parameter server. Recently, it is found that local gradients among different worker nodes are correlated. Therefore, distributed source coding (DSC) can be applied to increase communication efficiency by exploiting such correlation. However, it is highly non-trivial to exploite the gradient correlations in distributed learning due to the unknown and time-varying gradient correlation. In this paper, we first propose a DSC framework, named successive Wyner-Ziv coding, for distributed learning based on quantization and Slepian-Wolf (SW) coding. We prove that the proposed framework can achieve the theoretically minimum communication cost from an information theory perspective. We also propose a low-complexity and adaptive DSC for distributed learning, including a gradient statistics estimator, rate controller, and a log-likelihood ratio (LLR) computer. The gradient statistics estimator estimates the gradient statistics online based only on the quantized gradients at previous iterations, hence it does not introduce extra communication cost. The computation complexity of the rate controller and the LLR computer is reduced to a linear growth in the number of worker nodes by introducing a semi-analytical Monte Carlo simulation. Finally, we design a DSC-based distributed learning process and find that the extra delay introduced by DSC does not scale with the number of worker nodes.

关键词： Distance learning model aggregation distributed source coding Slepian-Wolf coding

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Effective Energy Adaptive and Consumption in Wireless Sensor Network Using distributed source coding and Sampling Techniques

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WIRELESS PERSONAL COMMUNICATIONS 2021年第2期118卷 1393-1404页

作者： Manikandan, S. Chinnadurai, M. EGS Pillay Engn Coll Nagapattinam Tamil Nadu India

Multimedia is the process of handling multiple medium of messages over network with high rate data services in wireless cellular area networks. Communication is the process of exchanging information form one service to another. In wireless networks are significantly growth of affecting network performance and energy consumption. The major problem is end to end delay in each node and meets the quality of services. The followings are considered for implementing wireless sensor network such as reduces the network delay, propagation delay and energy consumption. The senor node can sense the encoding value and reduce the network traffic delay using mitigation method. This paper propose a unique approach to provide simple routing services with reduced traffic delay, end to end delay network performance and to achieve better performance using distributed source coding and Effective Energy Consumption methods. In this paper we use optimal early detection algorithm for improving network performance and energy consumption problem. An iterative Shannon fano and Tuker method is used for finding optimal solution of each node values. Network Simulator-3 is used for simulating network environments and setup the experiments. Our proposed method shows high data rate, good performance and low energy consumptions. The results compare with existing methodologies and performance is good.

关键词： Wireless sensor networks Effective energy consumption distributed source coding End-to-end propagation delay Optimal early detection

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A New Achievable Rate-Distortion Region for distributed source coding

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IEEE TRANSACTIONS ON INFORMATION THEORY 2021年第7期67卷 4485-4503页

作者： Shirani, Farhad Pradhan, S. Sandeep North Dakota State Univ Elect & Comp Engn Dept Fargo ND 58103 USA Univ Michigan Elect Engn & Comp Sci Dept Ann Arbor MI 48109 USA

In this work, lossy distributed compression of a pair of correlated sources is considered. Conventionally, Shannon's random coding arguments - using randomly generated unstructured codebooks whose blocklength is taken to be asymptotically large - are used to derive achievability results. However, in some multi-terminal communications scenarios, using random codes with constant finite blocklength in certain coding architectures leads to improved achievable regions compared to the conventional approach. In other words, in some network communication scenarios, there is a finite optimal value in the blocklength of the randomly generated code used for distributed processing of information sources. Motivated by this, a coding scheme is proposed which consists of two codebook layers: i) the primary codebook which has constant finite blocklength, and ii) the secondary codebook whose blocklength is taken to be asymptotically large. The achievable performance is analyzed in two steps. In the first step, a characterization of an inner bound to the achievable region is derived in terms information measures which are functions of multi-letter probability distributions. In the next step, a computable single-letter inner-bound to the achievable region is extracted. It is shown through an example that the resulting rate-distortion region is strictly larger than the Berger-Tung achievable region.

关键词： Encoding Correlation Rate-distortion Decoding Markov processes source coding Quantization (signal) distributed source coding data compression common information finite block-length coding

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Lattice-Based Robust distributed source coding for Three Correlated sources

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IEEE TRANSACTIONS ON COMMUNICATIONS 2020年第7期68卷 4089-4103页

作者： Dumitrescu, Sorina Elzouki, Dania Chen, Jun McMaster Univ Dept Elect & Comp Engn Hamilton ON L8S 4K1 Canada

The problem of robust distributed source coding for three correlated sources is studied in this work. A lattice-based scheme is proposed and the analysis of its performance is provided in the high resolution regime. Special attention is paid to the degenerate case where the three sources are Gaussian and identical. In this case, our scheme is shown to achieve within an asymptotic gap of 0.069 bits in terms of rate per description from the information-theoretic limit of quadratic symmetric Gaussian multiple description coding with central and individual decoders, when the side distortion and the ratio between the central and side distortions both approach 0.

关键词： Lattices Decoding Robustness source coding Distortion Indexes Correlated sources distributed source coding lattice quantization asymptotic performance analysis high resolution multiple description coding

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Lattice-Based Robust distributed source coding

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IEEE TRANSACTIONS ON INFORMATION THEORY 2019年第3期65卷 1764-1781页

作者： Elzouki, Dania Dumitrescu, Sorina Chen, Jun McMaster Univ Dept Elect & Comp Engn Hamilton ON L8S 4L8 Canada

In this paper, we propose a lattice-based robust distributed source coding system for two correlated sources and provide a detailed performance analysis under the high resolution assumption. It is shown, among other things, that, in the asymptotic regime where: 1) the side distortion approaches 0 and 2) the ratio between the central and side distortions approaches 0, our scheme is capable of achieving the information-theoretic limit of quadratic multiple description coding when the two sources are identical, whereas a variant of the random coding scheme by Chen and Berger with Gaussian codes has a performance loss of 0.5 bits relative to this limit.

关键词： distributed source coding lattice quantization high resolution analysis

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distributed source coding of Hyperspectral Images Based on Three-Dimensional Wavelet

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JOURNAL OF THE INDIAN SOCIETY OF REMOTE SENSING 2018年第4期46卷 667-673页

作者： Wang, Xianghai Tao, Jingzhe Shen, Yutong Qin, Mingshuang Song, Chuanming Liaoning Normal Univ Sch Comp & Informat Technol Dalian 116029 Liaoning Peoples R China Liaoning Normal Univ Liaoning Key Lab Phys Geog & Geomat Dalian 116029 Liaoning Peoples R China

To reduce the possibility of poor efficiency and weak anti-error capability while encoding and transmitting hyperspectral images, we present a distributed source coding scheme for hyperspectral images based on three-dimensional (3D) set partitioning in hierarchical trees (SPIHT). First, the 3D wavelet transform is performed on the hyperspectral image. Thereafter, the low frequency section is regarded as the Key frame and the high frequency section as the Wyner-Ziv frame to enable independent SPIHT coding through different transmission channels. The Wyner-Ziv encoder uses Turbo channel coding to create high frequency information that reflects the details of the image with better anti-error capacity, while the low frequency information shows the main energy of the image. In this study, we used SPIHT coding to acquire a bit stream with quality scalability. Results show that the proposed scheme is more efficient during coding, while at the same time providing improved anti-error capability and quality scalability of the bit stream.

关键词： Hyperspectral remote sensing image distributed source coding 3D wavelet Channel coding

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