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破损压缩文件的修复还原

王刚 彭华 唐永旺

王刚, 彭华, 唐永旺. 破损压缩文件的修复还原[J]. 电子与信息学报, 2019, 41(8): 1831-1837. doi: 10.11999/JEIT180942
引用本文: 王刚, 彭华, 唐永旺. 破损压缩文件的修复还原[J]. 电子与信息学报, 2019, 41(8): 1831-1837. doi: 10.11999/JEIT180942
Gang WANG, Hua PENG, Yongwang TANG. Repair and Restoration of Corrupted Compressed Files[J]. Journal of Electronics & Information Technology, 2019, 41(8): 1831-1837. doi: 10.11999/JEIT180942
Citation: Gang WANG, Hua PENG, Yongwang TANG. Repair and Restoration of Corrupted Compressed Files[J]. Journal of Electronics & Information Technology, 2019, 41(8): 1831-1837. doi: 10.11999/JEIT180942

破损压缩文件的修复还原

doi: 10.11999/JEIT180942
基金项目: 国家自然科学基金(61572518, 61501516)
详细信息
    作者简介:

    王刚:男,1981年生,副教授,研究方向为信号分析、信息处理、模式识别

    彭华:男,1973年生,教授,研究方向为通信信号处理、软件无线电

    唐永旺:男,1981年生,讲师,研究方向为信息处理、协议分析

    通讯作者:

    彭华 phzttyw@126.com

  • 中图分类号: TP301

Repair and Restoration of Corrupted Compressed Files

Funds: The National Natural Science Foundation of China (61572518, 61501516)
  • 摘要: 数据压缩和解压缩已广泛应用于现代通信和数据传输领域。但是如何解压缩损坏的无损压缩文件仍然是一个挑战。针对在通用编码领域广泛使用的无损数据压缩算法,该文提出一种能够修复误码并解压还原损坏的LZSS文件的有效方法,并给出了理论依据。该方法通过利用编码器留下的残留冗余携带校验信息,在不损失任何压缩性能的情况下,能够修复LZSS压缩数据中的错误。所提方法不需要增加额外比特,也不改变编码规则和数据格式,所以与标准算法完全兼容。即采用具有错误修复能力的LZSS方案压缩的数据,仍然可以通过标准LZSS解码器进行解压。实验结果验证了所提算法的有效性和实用性。
  • 图  1  LZSS算法

    图  2  最长匹配前缀的多重性

    图  3  LZSR编码器(RSn表示分组Gn的校验码)

    图  4  LZSRD编码器(RSn表示分组Gn的校验码)

    图  5  最长匹配短语数量的平均值与文件长度的关系

    图  6  嵌入的比特数量与文件长度的关系

    图  7  错误修复能力的比较

    图  8  纠错率与压缩文件长度的关系

  • DRMOTA M and SZPANKOWSKI W. Redundancy of lossless data compression for known sources by analytic methods[J]. Foundations and Trends in Communications and Information Theory, 2016, 13(4): 277–417. doi: 10.1561/0100000090
    DAS S, BULL D M, and WHATMOUGH P N. Error-resilient design techniques for reliable and dependable computing[J]. IEEE Transactions on Device and Materials Reliability, 2015, 15(1): 24–34. doi: 10.1109/TDMR.2015.2389038
    MENGHWAR G D and MECKLENBRAUKER C F. Cooperative versus non-cooperative communications[C]. The 2nd International Conference on Computer, Control and Communication, Karachi, Pakistan, 2009: 1–3.
    HAMSCHIN B M, FERGUSON J D, and GRABBE M T. Interception of multiple low-power linear frequency modulated continuous wave signals[J]. IEEE Transactions on Aerospace and Electronic Systems, 2017, 53(2): 789–804. doi: 10.1109/TAES.2017.2665140
    KWON B, GONG M, and LEE S. Novel error detection algorithm for LZSS compressed data[J]. IEEE Access, 2017, 5: 8940–8947. doi: 10.1109/ACCESS.2017.2704900
    WANG Digang, ZHAO Xiaoqun, and SUN Qingquan. Novel fault-tolerant decompression method of corrupted Huffman files[J]. Wireless Personal Communications, 2018, 102(4): 2555–2574. doi: 10.1007/s11277-018-5277-5
    KOSTINA V, POLYANSKIY Y, and VERDú S. Variable-length compression allowing errors[J]. IEEE Transactions on Information Theory, 2015, 61(8): 4316–4330. doi: 10.1109/TIT.2015.2438831
    ZHANG Jie, YANG Enhui, and KIEFFER J C. A universal grammar-based code for lossless compression of binary trees[J]. IEEE Transactions on Information Theory, 2014, 60(3): 1373–1386. doi: 10.1109/TIT.2013.2295392
    KLEIN S T and SHAPIRA D. Practical fixed length Lempel-Ziv coding[J]. Discrete Applied Mathematics, 2014, 163: 326–333. doi: 10.1016/j.dam.2013.08.022
    KITAKAMI M and KAWASAKI T. Burst error recovery method for LZSS coding[J]. IEICE Transactions on Information and Systems, 2009, E92.D(12): 2439–2444. doi: 10.1587/transinf.e92.d.2439
    PEREIRA Z C, PELLENZ M E, SOUZA R D, et al. Unequal error protection for LZSS compressed data using Reed-Solomon codes[J]. IET Communications, 2007, 1(4): 612–617. doi: 10.1049/iet-com:20060530
    LAKHANI G. Reducing coding redundancy in LZW[J]. Information Sciences, 2006, 176(10): 1417–1434. doi: 10.1016/j.ins.2005.03.007
    PARK B, SAVOLDI A, GUBIAN P, et al. Recovery of damaged compressed files for digital forensic purposes[C]. 2008 International Conference on Multimedia and Ubiquitous Engineering, Busan, South Korea, 2008: 365–372. doi: 10.1109/MUE.2008.49.
    KOSTINA V, POLYANSKIY Y, and VERD S. Joint source-channel coding with feedback[J]. IEEE Transactions on Information Theory, 2017, 63(6): 3502–3515. doi: 10.1109/TIT.2017.2674667
    KEMPA D and KOSOLOBOV D. LZ-end parsing in compressed space[C]. 2017 Data Compression Conference, Snowbird, USA, 2017: 350–359.
    徐金甫, 刘露, 李伟, 等. 一种基于阵列配置加速比模型的无损压缩算法[J]. 电子与信息学报, 2018, 40(6): 1492–1498. doi: 10.11999/JEIT170900

    XU Jinfu, LIU Lu, LI Wei, et al. A new lossless compression algorithm based on array configuration speedup model[J]. Journal of Electronics &Information Technology, 2018, 40(6): 1492–1498. doi: 10.11999/JEIT170900
    DO H H, JANSSON J, SADAKANE K, et al. Fast relative Lempel-Ziv self-index for similar sequences[J]. Theoretical Computer Science, 2014, 532: 14–30. doi: 10.1016/j.tcs.2013.07.024
    ATALLAH M J and LONARDI S. Augmenting LZ-77 with authentication and integrity assurance capabilities[J]. Concurrency and Computation: Practice and Experience, 2004, 16(11): 1063–1076. doi: 10.1002/cpe.804
    REED I S and SOLOMON G. Polynomial codes over certain finite fields[J]. Journal of the Society for Industrial and Applied Mathematics, 1960, 8(2): 300–304. doi: 10.1137/0108018
    WARD M D and SZPANKOWSKI W. Analysis of a randomized selection algorithm motivated by the LZ'77 scheme[C]. The 1st Workshop on Analytic Algorithmics and Combinatorics, New Orleans, USA, 2004: 153–160.
    JACQUET P and SZPANKOWSKI W. Analytical depoissonization and its applications[J]. Theoretical Computer Science, 1998, 201(1/2): 1–62. doi: 10.1016/S0304-3975(97)00167-9
    The Canterbury corpus[EB/OL]. http://corpus.canterbury.ac.nz/descriptions/#cantrbry, 2018.
    The Calgary corpus[EB/OL]. http://corpus.canterbury.ac.nz/descriptions/#calgary, 2018.
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出版历程
  • 收稿日期:  2018-10-10
  • 修回日期:  2019-02-11
  • 网络出版日期:  2019-02-26
  • 刊出日期:  2019-08-01

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