高级搜索

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

基于边信息改进的分布式信源编码方案

陈建华 和志圆 王炯

陈建华, 和志圆, 王炯. 基于边信息改进的分布式信源编码方案[J]. 电子与信息学报, 2020, 42(7): 1678-1685. doi: 10.11999/JEIT190522
引用本文: 陈建华, 和志圆, 王炯. 基于边信息改进的分布式信源编码方案[J]. 电子与信息学报, 2020, 42(7): 1678-1685. doi: 10.11999/JEIT190522
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

基于边信息改进的分布式信源编码方案

doi: 10.11999/JEIT190522
基金项目: 国家自然科学基金(61861045)
详细信息
    作者简介:

    陈建华:男,1964年生,教授,博士生导师,研究方向为信息传输理论与应用

    和志圆:男,1994年生,硕士生,研究方向为分布式信源编码

    王炯:女,1994年生,硕士生,研究方向为Context建模

    通讯作者:

    陈建华 chenjh@ynu.edu.cn

  • 中图分类号: TN911.21; TN919.81

Distributed Source Coding Using Improved Side Information

Funds: The National Natural Science Foundation of China (61861045)
  • 摘要:

    针对现有的非对称分布式信源编码(DSC)方案均存在的在误比特率(BER)以及压缩率方面的不足,该文提出基于边信息改进的DSC(DSCUISI)方案。发送方对信源序列进行抽样,将序列分为抽样与未抽样子序列,利用算术编码器对未抽样子序列进行压缩,同时计算抽样子序列的伴随式。接收方利用边信息序列与未抽样子序列之间的相关性,对抽样符号进行估计,估计出的序列与原始抽样子序列的相关性得到改进。最后利用原始抽样子序列的伴随式与估计出的序列进行联合译码以重建原始抽样子序列。实验结果表明:与基于低密度奇偶校验码和算术码的DSC方案相比,该文所提方案在信源内部相关性较强时具有压缩率高、在信源间相关度不高时则有重建错误率低的特点,是一种高效、实用且易于实现的DSC方案。

  • 图  1  DSCUISI方案以及抽样过程

    图  2  信源输出序列与SI序列相关性的变化

    图  3  条件点为被抽取点的解决方法

    图  4  文献[8]和文献[11]与DSCUISI的BER对比

    图  5  解码图像质量实验

    表  1  概率统计算法

     输入:X and Y
     Initialize count(00000)=1,…,count(11111)=1
      Set i = 2 num=1
     while (i<=N-1) do
     if (i+1 mod k) = 0
      count(X[i-1] 0 Y[i-1] Y[i+1] Y[i])++
      count(X[i-1] 1 Y[i-1] Y[i+1] Y[i])++
     else if (i mod k) = 0
      continue
     else if (i-1 mod k) = 0
      count(0 X[i+1] Y[i-1] Y[i+1] Y[i])++
      count(1 X[i+1] Y[i-1] Y[i+1] Y[i])++
     else
        count(X[i-1] X[i+1] Y[i-1] Y[i+1] Y[i])++
     end if
      end while
      for num<=32
        calculating probability using count
      end for
     输出:probability distribution
    下载: 导出CSV

    表  2  压缩率(码率)对比结果

    Peppersp=0.0775, H(X|Y)=0.3925p=0.10759, H(X|Y)=0.4918
    文献[8]BER : LDPC=0.0193; ILDPC=0文献[8]BER : LDPC=0.1073; ILDPC=0.1158
    文献[11]: Rate=0.474487, BER=0.038486文献[11]: Rate=0.474487, BER=0.150906
    CDSCUISI方案的码率, k=3, k=4, k=6
    00.7000400.7278500.754701
    10.2982850.2745570.260146
    30.2171240.2034600.216251
    Lenap=0.076714, H(X|Y)=0.3808p=0.098148, H(X|Y)=0.4516
    文献[8]BER : LDPC=0.0076; ILDPC=0文献[8]BER : LDPC=0.0919; ILDPC=0.0625
    文献[11]: Rate=0.547241, BER=0.004826文献[11]: Rate=0.547241, BER=0.043842
    CDSCUISI方案的码率,k=3, k=4, k=6
    00.6214540.618760.638811
    10.2821160.2557480.238359
    30.2204300.2013500.212461
    Planep=0.080215, H(X|Y)=0.3277p=0.11145, H(X|Y)=0.4048
    文献[8]BER : LDPC=0.0290; ILDPC=0文献[8]BER : LDPC=0.1138; ILDPC=0.1238
    文献[11]: Rate=0.449249, BER=0.102680文献[11]: Rate=0.449249, BER=0.148754
    CDSCUISI方案的码率,k=3, k=4, k=6
    00.5766430.5936500.608581
    10.2969490.2792320.259157
    30.2083560.2055000.210581
    Boatsp=0.076576, H(X|Y)=0.3644p=0.101559, H(X|Y)=0.4403
    文献[8]BER : LDPC=0.0135; ILDPC=0文献[8]BER : LDPC=0.0972; ILDPC=0.0899
    文献[11]: Rate=0.488861, BER=0.073799文献[11]: Rate=0.488861, BER=0.145199
    CDSCUISI方案的码率,k=3, k=4, k=6
    00.6522940.6781240.700619
    10.3455310.3212380.306967
    30.2484420.2460970.254241
    Woman2p=0.073326, H(X|Y)=0.3630p=0.102539, H(X|Y)=0.4561
    文献[8]BER : LDPC=0.0029; ILDPC=0文献[8]BER : LDPC=0.0996; ILDPC=0.1006
    文献[11]: Rate=0.523987, BER=0.009167文献[11]: Rate=0.523987, BER=0.075237
    CDSCUISI方案的码率,k=3, k=4, k=6
    00.5828230.5976510.608176
    10.2304680.2021290.179202
    30.1742810.1546350.150198
    下载: 导出CSV
  • 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
图(5) / 表(2)
计量
  • 文章访问数:  2889
  • HTML全文浏览量:  1178
  • PDF下载量:  75
  • 被引次数: 0
出版历程
  • 收稿日期:  2019-07-11
  • 修回日期:  2020-03-17
  • 网络出版日期:  2020-04-15
  • 刊出日期:  2020-07-23

目录

    /

    返回文章
    返回