Adaptive Distributed Source Coding
We consider distributed source coding in the presence of hidden variables that parameterize the statistical dependence among sources. We derive the Slepian-Wolf bound and devise coding algorithms for a block-candidate model of this problem. The encoder sends, in addition to syndrome bits, a portion...
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| Vydáno v: | IEEE transactions on image processing Ročník 21; číslo 5; s. 2630 - 2640 |
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| Hlavní autoři: | , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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New York, NY
IEEE
01.05.2012
Institute of Electrical and Electronics Engineers The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 1057-7149, 1941-0042, 1941-0042 |
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| Abstract | We consider distributed source coding in the presence of hidden variables that parameterize the statistical dependence among sources. We derive the Slepian-Wolf bound and devise coding algorithms for a block-candidate model of this problem. The encoder sends, in addition to syndrome bits, a portion of the source to the decoder uncoded as doping bits. The decoder uses the sum-product algorithm to simultaneously recover the source symbols and the hidden statistical dependence variables. We also develop novel techniques based on density evolution (DE) to analyze the coding algorithms. We experimentally confirm that our DE analysis closely approximates practical performance. This result allows us to efficiently optimize parameters of the algorithms. In particular, we show that the system performs close to the Slepian-Wolf bound when an appropriate doping rate is selected. We then apply our coding and analysis techniques to a reduced-reference video quality monitoring system and show a bit rate saving of about 75% compared with fixed-length coding. |
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| AbstractList | We consider distributed source coding in the presence of hidden variables that parameterize the statistical dependence among sources. We derive the Slepian-Wolf bound and devise coding algorithms for a block-candidate model of this problem. The encoder sends, in addition to syndrome bits, a portion of the source to the decoder uncoded as doping bits. The decoder uses the sum-product algorithm to simultaneously recover the source symbols and the hidden statistical dependence variables. We also develop novel techniques based on density evolution (DE) to analyze the coding algorithms. We experimentally confirm that our DE analysis closely approximates practical performance. This result allows us to efficiently optimize parameters of the algorithms. In particular, we show that the system performs close to the Slepian-Wolf bound when an appropriate doping rate is selected. We then apply our coding and analysis techniques to a reduced-reference video quality monitoring system and show a bit rate saving of about 75% compared with fixed-length coding.We consider distributed source coding in the presence of hidden variables that parameterize the statistical dependence among sources. We derive the Slepian-Wolf bound and devise coding algorithms for a block-candidate model of this problem. The encoder sends, in addition to syndrome bits, a portion of the source to the decoder uncoded as doping bits. The decoder uses the sum-product algorithm to simultaneously recover the source symbols and the hidden statistical dependence variables. We also develop novel techniques based on density evolution (DE) to analyze the coding algorithms. We experimentally confirm that our DE analysis closely approximates practical performance. This result allows us to efficiently optimize parameters of the algorithms. In particular, we show that the system performs close to the Slepian-Wolf bound when an appropriate doping rate is selected. We then apply our coding and analysis techniques to a reduced-reference video quality monitoring system and show a bit rate saving of about 75% compared with fixed-length coding. We consider distributed source coding in the presence of hidden variables that parameterize the statistical dependence among sources. We derive the Slepian-Wolf bound and devise coding algorithms for a block-candidate model of this problem. The encoder sends, in addition to syndrome bits, a portion of the source to the decoder uncoded as doping bits. The decoder uses the sum-product algorithm to simultaneously recover the source symbols and the hidden statistical dependence variables. We also develop novel techniques based on density evolution (DE) to analyze the coding algorithms. We experimentally confirm that our DE analysis closely approximates practical performance. This result allows us to efficiently optimize parameters of the algorithms. In particular, we show that the system performs close to the Slepian-Wolf bound when an appropriate doping rate is selected. We then apply our coding and analysis techniques to a reduced-reference video quality monitoring system and show a bit rate saving of about 75% compared with fixed-length coding. |
| Author | Varodayan, D. Girod, B. Yao-Chung Lin |
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| Keywords | Performance evaluation Source coding Coding circuit Doping Distributed source signal Information rate Video technique Decoding Algorithm Information transmission Video signal processing Remote sensing Adaptive coding Image quality Monitoring system Quality control sum product algorithm Remote supervision |
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| References | zhao (ref14) 2003 ref12 ref15 ref31 ref30 ref11 ref10 ref2 ref1 ref16 ref19 ref18 varodayan (ref20) 2007 varodayan (ref33) 2010 chono (ref5) 2008 (ref32) 1992 dempster (ref17) 1977; 39 ref24 ref23 ref25 ref22 ref21 toffetti (ref4) 2005 aaron (ref9) 2002 ref27 ref29 ref8 ref7 ref6 garcacute a-fracute as (ref13) 2003 golomb (ref28) 1967 (ref26) 2004 zhu (ref3) 2003 |
| References_xml | – ident: ref27 doi: 10.1002/ecja.20188 – year: 2003 ident: ref13 article-title: LDPC codes for asymmetric compression of multi-terminal sources with hidden Markov correlation publication-title: Proc CTA Commun Netw Symp – year: 2004 ident: ref26 publication-title: ITU-T Recommendation J 240 Framework for Remote Monitoring of Transmitted Picture Signal-to-Noise Ratio Using Spread-Spectrum and Orthogonal Transform – ident: ref15 doi: 10.1109/LCOMM.2003.810001 – ident: ref30 doi: 10.1109/DCC.1997.582045 – ident: ref22 doi: 10.1109/18.910572 – year: 2005 ident: ref4 article-title: Image compression in a multi-camera system based on a distributed source coding approach publication-title: Proc Euro Signal Process Conf – ident: ref21 doi: 10.1016/j.image.2008.04.009 – ident: ref12 doi: 10.1016/j.sigpro.2006.03.012 – year: 2010 ident: ref33 publication-title: Adaptive Distributed Source Coding – ident: ref31 doi: 10.1109/TCSVT.2003.815168 – ident: ref1 doi: 10.1109/JPROC.2004.839619 – year: 2007 ident: ref20 article-title: Wyner-Ziv coding of stereo images with unsupervised learning of disparity publication-title: Proc Picture Coding Symp – ident: ref8 doi: 10.1109/4234.957380 – start-page: 252 year: 2002 ident: ref9 article-title: Compression with side information using turbo codes publication-title: Proc IEEE Data Compression Conf – ident: ref16 doi: 10.1214/aoms/1177697196 – year: 1967 ident: ref28 publication-title: Shift Register Sequences – year: 2003 ident: ref14 article-title: Turbo codes for symmetric compression of correlated binary sources with hidden Markov correlation publication-title: Proc CTA Commun Netw Symp – ident: ref24 doi: 10.1002/0471200611 – start-page: 30 year: 2003 ident: ref3 article-title: Distributed compression for large camera arrays publication-title: Proc IEEE Workshop Stat Signal Process – ident: ref29 doi: 10.4108/ICST.MOBIMEDIA2009.7532 – ident: ref6 doi: 10.1109/TIT.1973.1055037 – ident: ref23 doi: 10.1109/18.910577 – ident: ref25 doi: 10.1017/CBO9780511791338 – volume: 39 start-page: 1 year: 1977 ident: ref17 article-title: Maximum likelihood from incomplete data via the EM algorithm publication-title: J Roy Stat Soc Ser B doi: 10.1111/j.2517-6161.1977.tb01600.x – ident: ref10 doi: 10.1109/LCOMM.2002.804244 – start-page: 609 year: 2008 ident: ref5 article-title: Reduced-reference image quality estimation using distributed source coding publication-title: Proc IEEE Int Conf Multimedia Expo – ident: ref19 doi: 10.1109/DCC.2007.35 – ident: ref7 doi: 10.1109/GLOCOM.2001.965721 – ident: ref2 doi: 10.1109/TIP.2007.904949 – year: 1992 ident: ref32 publication-title: ITU-T Recommendation T 81 Digital Compression and Coding of Continuous-Tone Still Images – ident: ref11 doi: 10.1109/ACSSC.2005.1599952 – ident: ref18 doi: 10.1109/MMSP.2006.285257 |
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| SubjectTerms | Algorithms Applied sciences Approximation Codes Coding Coding, codes Complexity theory Data Compression - methods Decoders Decoding Density Detection, estimation, filtering, equalization, prediction Doping Entropy Exact sciences and technology Image Enhancement - methods Image Interpretation, Computer-Assisted - methods Image processing Information, signal and communications theory Mathematical models Monitoring Monitoring systems Parity check codes Photography - methods Reproducibility of Results Sensitivity and Specificity Signal and communications theory Signal processing Signal Processing, Computer-Assisted Signal, noise Source coding Studies sum product algorithm Telecommunications and information theory Vectors Video Recording - methods video signal processing |
| Title | Adaptive Distributed Source Coding |
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