Advanced Search
Volume 42 Issue 7
Jul.  2020
Turn off MathJax
Article Contents
Jianhua CHEN, Zhiyuan HE, Jiong WANG. Distributed Source Coding Using Improved Side Information[J]. Journal of Electronics & Information Technology, 2020, 42(7): 1678-1685. doi: 10.11999/JEIT190522
Citation: Jianhua CHEN, Zhiyuan HE, Jiong WANG. Distributed Source Coding Using Improved Side Information[J]. Journal of Electronics & Information Technology, 2020, 42(7): 1678-1685. doi: 10.11999/JEIT190522

Distributed Source Coding Using Improved Side Information

doi: 10.11999/JEIT190522
Funds:  The National Natural Science Foundation of China (61861045)
  • Received Date: 2019-07-11
  • Rev Recd Date: 2020-03-17
  • Available Online: 2020-04-15
  • Publish Date: 2020-07-23
  • Considering the shortcomings on the Bit Error Rate (BER) and the compression ratio of the existing asymmetric Distributed Source Coding (DSC) schemes, a scheme named Distributed Source Coding Using Improved Side Information (DSCUISI) is proposed. At the sender, the source sequence is sampled and divided into a sampled and an un-sampled sub-sequences. The un-sampled sub-sequence is compressed by arithmetic coder while the syndrome of the sampled sub-sequence is calculated. The receiver exploits the correlation between the side information and the un-sampled sub-sequence to estimate the sampled symbols, so that the correlation between the estimated sequence and the original sampled sub-sequence is improved. Finally, the syndromes and the estimated sequence are used to recover the sampled sub-sequence. Experiment results show that the DSCUISI can reach high compression ratio, when the correlation among neighboring symbols is strong. The BER of the reconstructed sequence can be kept low when the correlation between sources are weak. It is an efficient, practical DSC scheme and is easy to be implemented.

  • loading
  • YANG Hong, QING Linbo, HE Xiaohai, et al. Robust distributed video coding for wireless multimedia sensor networks[J]. Multimedia Tools and Applications, 2018, 77(4): 4453–4475. doi: 10.1007/s11042-016-4245-x
    YANG Jia, QING Linbo, ZENG Wenjun, et al. High-order statistical modeling based on a decision tree for distributed video coding[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2019, 29(5): 1488–1502. doi: 10.1109/TCSVT.2018.2840126
    HAGAG A, FAN Xiaopeng, and EL-SAMIE F E A. Hyperspectral image coding and transmission scheme based on wavelet transform and distributed source coding[J]. Multimedia Tools and Applications, 2017, 76(22): 23757–23776. doi: 10.1007/s11042-016-4158-8
    SLEPIAN D and WOLF J K. Noiseless coding of correlated information sources[J]. IEEE Transactions on Information Theory, 1973, 19(4): 471–480. doi: 10.1109/TIT.1973.1055037
    洪少华, 王琳. 基于原模图LDPC码的分布式联合信源信道编码[J]. 电子与信息学报, 2017, 39(11): 2594–2599. doi: 10.11999/JEIT170113

    HONG Shaohua and WANG Lin. Protograph LDPC based distributed joint source channel coding[J]. Journal of Electronics &Information Technology, 2017, 39(11): 2594–2599. doi: 10.11999/JEIT170113
    PRADHAN S S and RAMCHANDRAN K. Distributed Source Coding Using Syndromes (DISCUS): Design and construction[J]. IEEE Transactions on Information Theory, 2003, 49(3): 626–643. doi: 10.1109/TIT.2002.808103
    GARCIA-FRIAS J. Compression of correlated binary sources using turbo codes[J]. IEEE Communications Letters, 2001, 5(10): 417–419. doi: 10.1109/4234.957380
    LIVERIS A D, XIONG Zixiang, and GEORGHIADES C N. Compression of binary sources with side information at the decoder using LDPC codes[J]. IEEE Communications Letters, 2002, 6(10): 440–442. doi: 10.1109/LCOMM.2002.804244
    JIN Liqiang, YANG Pei, and YANG Hongwen. Distributed joint source-channel decoding using systematic polar codes[J]. IEEE Communications Letters, 2018, 22(1): 49–52. doi: 10.1109/LCOMM.2017.2768036
    GRANGETTO M, MAGLI E, and OLMO G. Distributed arithmetic coding[J]. IEEE Communications Letters, 2007, 11(11): 883–885. doi: 10.1109/LCOMM.2007.071172
    GRANGETTO M, MAGLI E, and OLMO G. Distributed arithmetic coding for the Slepian-Wolf problem[J]. IEEE Transactions on Signal Processing, 2009, 57(6): 2245–2257. doi: 10.1109/TSP.2009.2014280
    MALINOWSKI S, ARTIGAS X, GUILLEMOT C, et al. Distributed coding using punctured quasi-arithmetic codes for memory and memoryless sources[J]. IEEE Transactions on Signal Processing, 2009, 57(10): 4154–4158. doi: 10.1109/TSP.2009.2023359
    CAO Ying, SUN Lijuan, HAN Chong, et al. Improved side information generation algorithm based on naive Bayesian theory for distributed video coding[J]. IET Image Processing, 2018, 12(3): 354–360. doi: 10.1049/iet-ipr.2017.0892
    DASH B, RUP S, MOHAPATRA A, et al. Decoder driven side information generation using ensemble of MLP networks for distributed video coding[J]. Multimedia Tools and Applications, 2018, 77(12): 15221–15250. doi: 10.1007/s11042-017-5103-1
    VARODAYAN D, LIN Y C, GIROD B, et al. Adaptive distributed source coding[J]. IEEE Transactions on Image Processing, 2012, 21(5): 2630–2640. doi: 10.1109/TIP.2011.2175936
    罗瑜, 张珍珍. 一种方向插值预测变长编码的帧存有损压缩算法[J]. 电子与信息学报, 2019, 41(10): 2495–2500. doi: 10.11999/JEIT181195

    LUO Yu and ZHANG Zhenzhen. A lossy frame memory compression algorithm using directional interpolation prediction variable length coding[J]. Journal of Electronics &Information Technology, 2019, 41(10): 2495–2500. doi: 10.11999/JEIT181195
    WEISSMAN T, ORDENTLICH E, SEROUSSI G, et al. Universal discrete denoising: Known channel[J]. IEEE Transactions on Information Theory, 2005, 51(1): 5–28. doi: 10.1109/TIT.2004.839518
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(5)  / Tables(2)

    Article Metrics

    Article views (2938) PDF downloads(77) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return