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22nd SPIE Conference on Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXII

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

ISBN: (纸本)9781510600812

A lossless compression algorithm of hyperspectral image based on distributed source coding is proposed, which is used to compress the spaceborne hyperspectral data effectively. In order to make full use of the intra-frame correlation and inter-frame correlation, the prediction error block scheme are introduced. Compared with the scalar coset based distributed compression method (s-DSC) proposed by E. Magli et al., that is, the bitrate of the whole block is determined by its maximum prediction error, and the s-DSC-classify scheme proposed by Song Juan that is based on classification and coset coding, the prediction error block scheme could reduce the bitrate efficiently. Experimental results on hyperspectral images show that the proposed scheme can offer both high compression performance and low encoder complexity and decoder complexity, which is available for on-board compression of hyperspectral images.

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

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distributed Compression of Correlated sources using Systematic Polar Codes 9

Distributed Compression of Correlated Sources using Systemat...

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9th International Symposium on Turbo Codes and Iterative Information Processing (ISTC)

作者： Yaacoub, Charles Sarkis, Malak Holy Spirit Univ Kaslik USEK Fac Engn Dept Telecommun POB 446 Jounieh Lebanon

ISBN: (纸本)9781509034017

Since their invention in 2009, Polar Codes have emerged as new near-capacity channel codes with a great potential of replacing turbo codes and low density parity check codes in a wide range of applications. This paper investigates the use of systematic polar codes in a distributed source coding application. A system model is proposed for the compression of distributed sources with side information at the decoder, along with a theoretical approach for estimating the ideal compression ratio. The system performance is assessed in terms of compression efficiency and bit error rate, for different code parameters and source correlation levels. Simulation results show the efficiency of systematic polar codes in distributed source coding, which makes them suitable for practical distributed compression applications such as wireless sensor networks and distributed video coding. Furthermore, despite the fact that error correction performance of polar codes improves with larger blocks, results show that for a compression rate below 0.4 (i.e. compression stronger than 2.5:1), better error rates can be achieved with smaller blocks.

关键词： channel coding compression distributed source coding entropy systematic polar codes

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distributed Variable-Rate Quantized Compressed Sensing in Wireless Sensor Networks 17

Distributed Variable-Rate Quantized Compressed Sensing in Wi...

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17th IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)

作者： Leinonen, Markus Codreanu, Marian Juntti, Markku Univ Oulu Ctr Wireless Commun POB 4500 Oulu 90014 Finland

ISBN: (纸本)9781509017492

This paper addresses distributed finite-rate quantized compressed sensing (QCS) acquisition of correlated sparse sources in wireless sensor networks. We propose a distributed variable-rate QCS compression method with complexity constrained encoding to minimize a weighted sum of the mean square error distortion of the signal reconstruction and the average encoding rate. The variable-rate coding is realized via entropy-constrained vector quantization, whereas the restrained encoding complexity is obtained via vector pre-quantization of CS measurements. We derive necessary optimality conditions for the system blocks for two-sensor case. Numerical results show that our proposed method efficiently exploits the signal correlation, and achieves superior distortion-rate compression performance.

关键词： Terms Compressed sensing distributed source coding sparse signals vector quantization entropy coding

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SIDE-INFORMATION-ADAPTIVE distributed source coding

SIDE-INFORMATION-ADAPTIVE DISTRIBUTED SOURCE CODING

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IEEE International Conference on Image Processing

作者： Varodayan, David Girod, Bernd Stanford Univ Informat Syst Lab Stanford CA 94305 USA

ISBN: (纸本)9781424479948

Consider distributed source coding in which each block of the source at the encoder is associated with multiple candidates for side information at the decoder, just one of which is statistically dependent on the source block. Our encoder codes the source as syndrome bits and also sends a portion of it uncoded as doping bits. The decoder adaptively discovers the best side information candidates for each block of the source. The main contribution is a method based on density evolution to analyze and design the coding performance. Experimental results show that the density evolution technique is accurate in modeling the codec and optimizing its doping rate.

关键词： distributed source coding sum-product algorithm density evolution

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Adaptive distributed source coding over Erasure Channels using Particle-based Belief Propagation

Adaptive Distributed Source Coding over Erasure Channels usi...

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IEEE Global Telecommunications Conference (GLOBECOM 2010)

作者： Cui, Lijuan Wang, Shuang Cheng, Samuel Univ Oklahoma Sch Elect & Comp Engn Tulsa OK 74135 USA

ISBN: (纸本)9781424456383

This paper addresses the problem of distributed source coding (DSC) of binary sources with unknown varying correlation statistics over erasure channels. We propose an adaptive asymmetric Slepian-Wolf (SW) decoding scheme using particle-based belief propagation (BP) based on Raptor codes. We show through the experiments that the proposed algorithm can simultaneously reconstruct the compressed sources and estimate the correlation between source and side information. Moreover, compared to the conventional Raptor decoder, the proposed approach can achieve a higher compression ratio and provide stronger erasure protection under unknown varying correlation statistics. The ability to estimate the statistical correlation in the code structure makes our approach very useful for real applications.

关键词： distributed source coding Raptor codes Adaptive decoding

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RATE-DISTORTION BASED RECONSTRUCTION OPTIMIZATION IN distributed source coding FOR INTERACTIVE MULTIVIEW VIDEO STREAMING

RATE-DISTORTION BASED RECONSTRUCTION OPTIMIZATION IN DISTRIB...

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IEEE International Conference on Image Processing

作者： Cheung, Ngai-Man Ortega, Antonio Cheung, Gene Stanford Univ Stanford CA 94305 USA Univ Southern Calif Los Angeles CA 90089 USA Natl Inst Informat Tokyo Japan

ISBN: (纸本)9781424479948

Interactive multiview video streaming (IMVS) is an application where, as the streaming multiview video is played back in time, an observer iteratively requests one of many available views at the server. In response, the server sends the appropriate pre-encoded data to the observer, with data chosen for transmission depending on the specific transmitted data available in the observer's cache. The primary challenge in IMVS is to design a structure for the pre-encoded multiview data, so that during an IMVS streaming session, the transmission rate is appropriately traded off with the pre-encoded data storage size. Previously, we have developed novel distributed source coding (DSC) based frame configurations to optimize the said tradeoff, outperforming periodical insertions of I-frames in both transmission and storage costs. In this paper, we show that by exploiting the freedom to choose a target decoded frame at a view switching point for DSC, further performance gains can be achieved: by up to 0.7 dB in our experiments.

关键词： Interactive multiview video streaming distributed source coding

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distributed source coding AND DISPERSIVE INFORMATION ROUTING : AN INTEGRATED APPROACH WITH NETWORKING AND DATABASE APPLICATIONS

DISTRIBUTED SOURCE CODING AND DISPERSIVE INFORMATION ROUTING...

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European Signal Processing Conference

作者： Kumar Viswanatha Emrah Akyol Sharadh Ramaswamy Kenneth Rose Department of Electrical and Computer Engineering University of California

This paper considers the design of optimal joint compression-routing schemes for networks with correlated sources and multiple sinks. Such a setting is typically encountered in sensor networks. It is sometimes tempting to assume that an optimal distributed compression scheme followed by standard routing (as designed for independent sources) would be nearly optimal. We instead propose a joint design approach that integrates distributed source coding with a mechanism called dispersive information routing. Unlike network coding, dispersive information routing can be realized using conventional routers without recourse to recoding at intermediate nodes. We also point out the direct connections between dispersive information routing and a related problem in sensor network databases, namely, fusion coding for selective retrieval. We propose an efficient practical design strategy, variants of which are adopted for each of the two problems. Simulation results provide evidence for substantial gains over conventional schemes.

关键词： Compression distributed source coding Routing Sensor networks Database retrieval

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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 123114-123114页

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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distributed Lossless coding Techniques for Hyperspectral Images

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IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING 2015年第6期9卷 977-989页

作者： Zhang, Jinlei Li, Houqiang Chen, Chang Wen Univ Sci & Technol China CAS Key Lab Technol Geospatial Informat Proc & Ap Hefei 230027 Anhui Peoples R China SUNY Buffalo Dept Comp Sci & Engn Buffalo NY 14260 USA

In this paper, we present a novel distributed coding scheme for lossless, progressive and low complexity compression of hyperspectral images. Hyperspectral images have several unique requirements that are vastly different from consumer images. Among them, lossless compression, progressive transmission, and low complexity onboard processing are three most prominent ones. To satisfy these requirements, we design a distributed coding scheme that shifts the complexity of data decorrelation to the decoder side to achieve lightweight onboard processing after image acquisition. At the encoder, the images are subsampled in order to facilitate successive encoding and progressive transmission. At the decoder, we generate the side information with adaptive region-based predictor by taking full advantage of the decoded subsampled images and previously decoded neighboring bands based on the assumptions that the objects appearing in different bands are highly correlated. The proposed progressive transmission via subsampling enables the spectral correlation to be refined successively, resulting in gradually improved decoding performance of higher-resolution layers as more sub-images are decoded. Experimental results on the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data demonstrate that the proposed scheme is able to achieve competitive compression performance comparing with the-state-of-the-art 3D schemes, including existing distributed source coding (DSC) schemes. The proposed scheme has even lower encoding complexity than that of the conventional 2D schemes.

关键词： Lossless compression distributed source coding hyperspectral images low complexity encoding

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distributed arithmetic coding with interval swapping

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SIGNAL PROCESSING 2015年 116卷 29-37页

作者： Zhou, Junwei Wong, Kwok-Wo Yang, Yanchao Wuhan Univ Technol Sch Comp Sci & Technol Wuhan Peoples R China City Univ Hong Kong Dept Elect Engn Hong Kong Hong Kong Peoples R China

In distributed source coding, ambiguity is usually introduced in the encoding process as it allows multiple plaintext sequences encoded to the same codeword. All these plaintext sequences are decodable and are considered as candidates at the decoder. With the help of side information, the decoder is able to determine which sequence in the candidate set is the best choice. Both the cardinality and the minimum Hamming distance of the candidate set are significant to the decoding performance. In this paper, a Slepian-Wolf code based on arithmetic coding is studied. By employing the interval swapping technique, a linear code is incorporated into binary arithmetic coding. The incorporated linear code improves the minimum Hamming distance within the candidate set which leads to a lower bit error probability. Moreover, binary arithmetic coding exploits the a priori knowledge of the source to reduce the cardinality of the candidate set. Simulation results show that this approach leads to superior performance for moderately skewed sources with linear encoding complexity, which meets the low power consumption requirement of applications such as wireless sensor networks and low-complexity multimedia compression. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Data compression Correlated data distributed source coding Slepian-Wolf coding

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