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distributed coding of Quantized Random Projections

IEEE TRANSACTIONS ON SIGNAL PROCESSING 2020年 68卷 5924-5939页

作者： Goukhshtein, Maxim Boufounos, Petros T. Koike-Akino, Toshiaki Draper, Stark C. Univ Toronto Dept Elect & Comp Engn Toronto ON Canada Mitsubishi Elect Res Labs MERL Cambridge MA 02139 USA

In this paper we propose a new framework for distributed source coding of structured sources, such as sparse signals. Our framework capitalizes on recent advances in the theory of linear inverse problems and signal representations using incoherent projections. Our approach acquires and quantizes incoherent linear measurements of the signal, which are represented as separate bitplanes. Each bitplane is coded using a distributed source code of the appropriate rate, and transmitted. The decoder, starts from the least significant biplane and, using a prediction of the signal as side information, iteratively recovers each bitplane based on the source prediction and the signal, assuming all the previous bitplanes of lower significance have already been recovered. We provide theoretical results guiding the rate selection, relying only on the least squares prediction error of the source. This is in contrast to existing approaches which rely on difficult-to-estimate information-theoretic metrics to set the rate. We validate our approach using simulations on remote-sensing multispectral images, comparing them with existing approaches of similar complexity.

关键词： distributed source coding lossy compression quantization sparsity compressed sensing syndrome decoding low complexity encoder side information

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Q-Ary distributed Arithmetic coding for Uniform Q-Ary sources

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IEEE TRANSACTIONS ON INFORMATION THEORY 2023年第1期69卷 47-74页

作者： Fang, Yong Changan Univ Sch Informat Engn Xian 710064 Shaanxi Peoples R China

Laplacian distribution is widely used to model the differences between correlated continuous or nonbinary signals, e.g., the predictive residues of videos. Usually, distributed coding of correlated nonbinary sources, e.g., distributed video coding, is implemented by binary or nonbinary Low-Density Parity-Check (LDPC) codes. In this paper, as an alternative, we attempt using nonbinary distributed Arithmetic coding (DAC) to implement distributed coding of uniform nonbinary sources with Laplace-distributed correlation. To analyze Q -ary DAC for uniform Q -ary sources, following the methodology developed in our prior work for binary DAC, we define and deduce Coset Cardinality Spectrum (CCS) from both fixed-length and variable-length perspectives, whose physical meanings are explained in detail;while for binary DAC, our prior work totally ignored the subtle difference between these two perspectives. Compared with binary DAC, an important advantage of nonbinary DAC is that, the mapping from source symbols to coding intervals is so flexible that there are many parameters that can be tuned to achieve better performance;while for binary DAC, there are very few tunable parameters, making it very hard to achieve better performance. This paper proposes a simple method to map source symbols onto coding intervals, which results in a very lightweight codec, while possessing a good Manhattan distance distribution. Then this paper deduces the formula of path metrics for decoder design by making use of CCS. All theoretical analyses are perfectly verified by simulation results. Most important, experimental results show that $Q$ -ary DAC achieves significantly better performance than LDPC codes for distributed coding of uniform $Q$ -ary sources with Laplace-distributed correlation.

关键词： distributed source coding Slepian-Wolf coding distributed arithmetic coding coset cardinality spectrum nonbinary sources

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Trends and perspectives in image and video coding

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PROCEEDINGS OF THE IEEE 2005年第1期93卷 6-17页

作者： Sikora, T Tech Univ Berlin D-10587 Berlin Germany

The objective of the paper is to provide an overview on recent trends and future perspectives in image and video coding. Here, I review and the rapid development in the field during the past 40 years and outline current state-of-the art strategies for coding images and videos. These and other coding algorithms are discussed in the context of international JPEG, JPEG 2000, MPEG-1/2/4, and H.261/3/4 standards. Novel techniques targeted at achieving higher compression gains, error robustness, and network/device adapt-ability are described and discussed.

关键词： discrete cosine transform (DCT) distributed source coding embedded coding error concealment image coding International Telecommunication Union-Telecommunications (ITU-T) Joint Photographic Experts Group (JPEG) motion compensation JPEG 2000 Motion Picture Experts Group (MPEG) standardization video coding wavelets

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Intersensor Collaboration in distributed Quantization Networks

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IEEE TRANSACTIONS ON COMMUNICATIONS 2013年第9期61卷 3931-3942页

作者： Sun, John Z. Goyal, Vivek K. MIT Dept Elect Engn & Comp Sci Cambridge MA 02139 USA MIT Elect Res Lab Cambridge MA 02139 USA

Several key results in distributed source coding offer the intuition that little improvement in compression can be gained from intersensor communication when the information is coded in long blocks. However, when sensors are restricted to code their observations in small blocks (e. g., one) or desire fidelity of a computation applied to source realizations, intelligent collaboration between sensors can greatly reduce distortion. For networks where sensors are allowed to "chat" using a side channel that is unobservable at the fusion center, we provide asymptotically-exact characterization of distortion performance and optimal quantizer design in the high-resolution (low-distortion) regime using a framework called distributed functional scalar quantization (DFSQ). The key result is that chatting can dramatically improve performance even when intersensor communication is at very low rate. We also solve the rate allocation problem when communication links have heterogeneous costs and provide a detailed example to demonstrate the theoretical and practical gains from chatting. This example for maximum computation gives insight on the gap between chatting and distributed networks, and how to optimize the intersensor communication.

关键词： distributed source coding high-resolution quantization sensor networks side information

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On rate-constrained distributed estimation in unreliable sensor networks

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IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS 2005年第4期23卷 765-775页

作者： Ishwar, P Puri, R Ramchandran, K Pradhan, SS Univ Calif Berkeley Dept Elect Engn & Comp Sci Berkeley CA 94720 USA Univ Michigan Dept Elect Engn & Comp Sci Ann Arbor MI 48103 USA

We study the problem of estimating a physical process at a central processing unit (CPU) based on noisy measurements collected from a distributed, bandwidth-constrained, unreliable, network of sensors, modeled as an erasure network of unreliable "bit-pipes" between each sensor and the CPU. The CPU is guaranteed to receive data from a minimum fraction of the sensors and is tasked with optimally estimating the physical process under a specified distortion criterion. We study the noncollaborative (i.e., fully distributed) sensor network regime, and derive an information-theoretic achievable rate-distortion region for this network based on distributed source-coding insights. Specializing these results to the Gaussian setting and the mean-squared-error (MSE) distortion criterion reveals interesting robust-optimality properties of the solution. We also study the regime of clusters of collaborative sensors, where we address the important question: given a communication rate constraint between the sensor clusters and the CPU, should these clusters transmit their "raw data" or some low-dimensional "local estimates"? For a broad set of distortion criteria and sensor correlation statistics, we derive conditions under which rate-distortion-optimal compression of correlated cluster-observations separates into the tasks of dimension-reducing local estimation followed by optimal distributed compression of the local estimates.

关键词： decentralized vector-quantization distributed estimation distributed source coding information fusion

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distributed lossless compression algorithm for hyperspectral images based on the prediction error block and multiband prediction

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OPTICAL ENGINEERING 2016年第12期55卷

作者： Li, Yongjun Li, Yunsong Song, Juan Liu, Weijia Li, Jiaojiao Xidian Univ Sch Telecommun Engn State Key Lab Integrated Serv Networks 2 South Taibai StHitech Dev Zone Xian 710071 Peoples R China Xidian Univ Sch Telecommun Engn Joint Lab High Speed Multisource Image Coding & P 2 South Taibai StHitech Dev Zone Xian 710071 Peoples R China Xidian Univ Sch Software 2 South Taibai StHitech Dev Zone Xian 710071 Peoples R China

We address the problem of the lossless compression of hyperspectral images and present two efficient algorithms inspired by the distributed source coding principle, which perform the compression by means of the blocked coset coding. In order to make full use of the intraband and interband correlation, the prediction error block scheme and the multiband prediction scheme are introduced in the proposed algorithms. In the proposed algorithms, the prediction error of each 16 x 16 pixel block is partitioned into prediction error blocks of size 4 x 4. The bit rate of the pixels corresponding to the 4 x 4 prediction error block is determined by its maximum prediction error. This processing takes advantage of the local correlation to reduce the bit rate efficiently and brings the negligible increase of additional information. In addition to that, the proposed algorithms can be easily parallelized by having different 4 x 4 blocks compressed at the same time. Their performances are evaluated on AVIRIS images and compared with several existing algorithms. The experimental results on hyperspectral images show that the proposed algorithms have a competitive compression performance with existing distributed compression algorithms. Moreover, the proposed algorithms can provide low-codec complexity and high parallelism, which are suitable for onboard compression. (C) 2016 Society of Photo-Optical Instrumentation Engineers (SPIE)

关键词： hyperspectral images lossless compression distributed source coding coset coding block

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Interactive Encoding and Decoding Based on Binary LDPC Codes With Syndrome Accumulation

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IEEE TRANSACTIONS ON INFORMATION THEORY 2013年第5期59卷 3068-3103页

作者： Meng, Jin Yang, En-Hui Univ Waterloo Dept Elect & Comp Engn Waterloo ON N2L 3G1 Canada

Interactive encoding and decoding based on binary low-density parity-check codes with syndrome accumulation (SA-LDPC-IED) is proposed and investigated. Assume that the source alphabet is GF(2) and the side information alphabet is finite. It is first demonstrated how to convert any classical universal lossless code C-n (with block length n and side information available to both the encoder and decoder) into a universal SA-LDPC-IED scheme. It is then shown that with n the word error probability approaching 0 subexponentially with, the compression rate (including both the forward and backward rates) of the resulting SA-LDPC-IED scheme is upper bounded by a functional of that of C-n, which in turn approaches the compression rate of C-n for each and every individual sequence pair (x(n), y(n)) and the conditional entropy rate (H (X vertical bar Y)for any stationary, ergodic source and side information (X, Y) as the average variable node degree (l) over bar of the underlying LDPC code increases without bound. When applied to the class of binary source and side information (X, Y) correlated through a binary symmetrical channel with crossover probability unknown to both the encoder and decoder, the resulting SA-LDPC-IED scheme can be further simplified, yielding even improved rate performance versus the bit error probability when (l) over bar is not large. Simulation results (coupled with linear time belief propagation decoding) on binary source-side information pairs confirm the theoretic analysis and further show that the SA-LDPC-IED scheme consistently outperforms the Slepian-Wolf coding scheme based on the same underlying LDPC code. As a by-product, probability bounds involving LDPC established in the course are also interesting on their own and expected to have implications on the performance of LDPC for channel coding as well.

关键词： Belief propagation (BP) decoding distributed source coding entropy interactive encoding and decoding (IED) low-density parity-check (LDPC) code rateless Slepian-Wolf coding (SWC) syndrome accumulation (SA)

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Hash-Based Identification of Sparse Image Tampering

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2009年第11期18卷 2491-2504页

作者： Tagliasacchi, Marco Valenzise, Giuseppe Tubaro, Stefano Politecn Milan Dipartimento Elettron & Informat I-20133 Milan Italy

In the last decade, the increased possibility to produce, edit, and disseminate multimedia contents has not been adequately balanced by similar advances in protecting these contents from unauthorized diffusion of forged copies. When the goal is to detect whether or not a digital content has been tampered with in order to alter its semantics, the use of multimedia hashes turns out to be an effective solution to offer proof of legitimacy and to possibly identify the introduced tampering. We propose an image hashing algorithm based on compressive sensing principles, which solves both the authentication and the tampering identification problems. The original content producer generates a hash using a small bit budget by quantizing a limited number of random projections of the authentic image. The content user receives the (possibly altered) image and uses the hash to estimate the mean square error distortion between the original and the received image. In addition, if the introduced tampering is sparse in some orthonormal basis or redundant dictionary, an approximation is given in the pixel domain. We emphasize that the hash is universal, e. g., the same hash signature can be used to detect and identify different types of tampering. At the cost of additional complexity at the decoder, the proposed algorithm is robust to moderate content-preserving transformations including cropping, scaling, and rotation. In addition, in order to keep the size of the hash small, hash encoding/decoding takes advantage of distributed source codes.

关键词： Compressive sensing distributed source coding image hashing

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Minimal Information Exchange for Secure Image Hash-Based Geometric Transformations Estimation

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IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 2020年 15卷 3482-3496页

作者： Guerrini, Fabrizio Dalai, Marco Leonardi, Riccardo Univ Brescia CNIT Dept Informat Engn I-25123 Brescia Italy

Signal processing applications dealing with secure transmission are enjoying increasing attention lately. This paper provides some theoretical insights as well as a practical solution for transmitting a hash of an image to a central server to be compared with a reference image. The proposed solution employs a rigid image registration technique viewed in a distributed source coding perspective. In essence, it embodies a phase encoding framework to let the decoder estimate the transformation parameters using a very modest amount of information about the original image. The problem is first cast in an ideal setting and then it is solved in a realistic scenario, giving more prominence to low computational complexity in both the transmitter and receiver, minimal hash size, and hash security. Satisfactory experimental results are reported on a standard images set.

关键词： Image registration Cryptography Bandwidth Forensics Information security source coding Phase spectrum secure communication Fourier-Mellin transform rigid image registration distributed source coding image hashing

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Dynamic Compression-Transmission for Energy-Harvesting Multihop Networks With Correlated sources

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IEEE-ACM TRANSACTIONS ON NETWORKING 2014年第6期22卷 1729-1741页

作者： Tapparello, Cristiano Simeone, Osvaldo Rossi, Michele Univ Padua Dept Informat Engn I-35131 Padua Italy NJIT CWCSPR Newark NJ 07102 USA

Energy-harvesting wireless sensor networking is an emerging technology with applications to various fields such as environmental and structural health monitoring. A distinguishing feature of wireless sensors is the need to perform both source coding tasks, such as measurement and compression, and transmission tasks. It is known that the overall energy consumption for source coding is generally comparable to that of transmission, and that a joint design of the two classes of tasks can lead to relevant performance gains. Moreover, the efficiency of source coding in a sensor network can be potentially improved via distributed techniques by leveraging the fact that signals measured by different nodes are correlated. In this paper, a data-gathering protocol for multihop wireless sensor networks with energy-harvesting capabilities is studied whereby the sources measured by the sensors are correlated. Both the energy consumptions of source coding and transmission are modeled, and distributed source coding is assumed. The problem of dynamically and jointly optimizing the source coding and transmission strategies is formulated for time-varying channels and sources. The problem consists in the minimization of a cost function of the distortions in the source reconstructions at the sink under queue stability constraints. By adopting perturbation-based Lyapunov techniques, a close-to-optimal online scheme is proposed that has an explicit and controllable tradeoff between optimality gap and queue sizes. The role of side information available at the sink is also discussed under the assumption that acquiring the side information entails an energy cost.

关键词： Data gathering distributed source coding energy harvesting Lyapunov optimization wireless sensor networks

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