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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
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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

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