Abstract
Data compression and decompression have been widely applied in modern communication and data transmission fields. But how to decompress corrupted lossless compressed files is still a challenge. This paper presents an effective method to decompress corrupted LZSS files. It is achieved by utilizing source prior information and heuristic method. In this paper, we propose to use compression coding rules and grammar rules as the source prior information. Based on the prior information, we establish a mathematical model to detect error bits and estimate the rough range of the error bits. As for error correction, a heuristic method is developed to determine the accurate positions of error bits and correct the errors. The experimental results demonstrate that the proposed FTD method can achieve a correction rate of 96.45% for corrupted LZSS files when successfully decompressed. More importantly, the proposed method is a general model that can be applied to decompress various types of lossless compressed files of which the original files are natural language texts.




Similar content being viewed by others
References
Chen, S.-L., Liu, T.-Y., Shen, C.-W., & Tuan, M.-C. (2016). VLSI implementation of a cost-efficient near-lossless CFA image compressor for wireless capsule endoscopy. IEEE Access, 4, 10235–10245.
Jiang, F., Ji, X.-D., Hu, C.-J., Liu, S.-H., & Zhao, D.-B. (2014). Compressed vision information restoration based on cloud prior and local prior. IEEE Access, 2, 1117–1127.
Shanmugasundaram, S., & Lourdusamy, R. (2011). A comparative study of text compression algorithms. International Journal of Wisdom Based Computing, 3, 68–76.
Kuruppu, S., Beresford-Smith, B., Conway, T., & Zobel, J. (2012). Iterative dictionary construction for compression of large DNA data sets. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 9(1), 137–149.
Crystal, D. (2011). Dictionary of linguistics and phonetics (pp. 406–407). Hoboken: Wiley.
Barbieri, M. (2008). The codes of life (pp. 379–392). Hoboken: Wiley.
Hilbert, M., & Lopez, P. (2011). The world’s technological capacity to store, communicate, and compute information. Science, 332(6025), 60–65.
Menghwar, G. D. & Mecklenbrauker, C. F. (2009). Cooperative versus noncooperative communications. In International conference on computer, control and communication (pp. 1–3).
Ma, G. -F., Wang, Z. -X., & Cheng, Y. -S. (2015). Recovery of evidence and the judicial identification of electronic data based on ExFAT. In Proceedings of the 7th IEEE international conference cyber-enabled distributed computing and knowledge discovery (CyberC) (pp. 66–71).
Das, S., Bull, D. M., & Whatmough, P. N. (2015). Error-resilient design techniques for reliable and dependable computing. IEEE Transactions on Device and Materials Reliability, 15(1), 24–34.
Murin, Y., Dabora, R., & Gunduz, D. (2014). On joint source-channel coding for correlated sources over multiple-access relay channels. IEEE Transactions on Information Theory, 60(10), 6231–6253.
Stone, J. V. (2015). Information Theory: A Tutorial Introduction (pp. 61–71). England: Sebtel Press.
MacKay, D. J. (2003). Information theory, inference and learning algorithms (pp. 32–33). Cambridge: Cambridge University Press.
Sayood, K. (2012). Introduction to data compression (pp. 5–6). Central Tablelands: Newnes.
Huang, W. -J., & McCluskey, E. J. (2000). Transient errors and rollback recovery in LZ compression. In Proceedings of the IEEE Pacific rim international symposium dependable computing (pp. 128–135).
Kostina, V., Polyanskiy, Y., & Verd, S. (2017). Joint source-channel coding with feedback. IEEE Transactions on Information Theory, 63(6), 3502–3515.
Zhou, R.-S., & Li, S.-H. (2005). Study on recovery of zip archive data. Computer Developement Applications, 18(10), 2–3.
Park, B., Savoldi, A., Gubin, P., et al. (2005) Recovery of damaged compressed files for digital forensic purposes. In Proceedings of the IEEE international conference multimedia ubiquitous engineering (MUE) (pp. 365–372).
Kitakami, M., & Kawasaki, T. (2009). Burst error recovery method for LZSS coding. IEICE Transactions on Information and Systems, 92(12), 2439–2444.
Pereira, Z. C., Pellenz, M. E., Souza, R. D., & Siqueira, M. A. (2007). Unequal error protection for LZSS compressed data using Reed-Solomon codes. IET Communications, 1(4), 612–617.
Moffat, A., & Turpin, A. (2002). Compression and coding algorithms. Dordrecht: Kluwer Academic Publishers.
Huffman, W. C., & Pless, V. (2003). Fundamentals of error-correcting codes. Cambridge: Cambridge University Press.
Kwon, B., Gong, M., & Lee, S. (2017). Novel error detection algorithm for LZSS compressed data. IEEE Access, 5, 2169–3536.
Chen, Y.-X., et al. (2010). Novel error resilient decoding algorithm for LZW based residual source redundancy. Journal of Wuhan University of Technology, 32(10), 159–163.
Storer, J. A., & Szymanski, T. G. (1982). Data compression via textual substitution. Journal of the ACM, 29(4), 928–951.
Manning, C. D., Raghavan, P., & Schutze, H. (2009). Introduction to Information Retrieval (pp. 237–252). Cambridge: Cambridge University Press.
Kundeti, V. & Rajasekaran, S. (2008). Extending the four Russian algorithm to compute the edit script in linear space. In Proceedings of the international conference computational science (ICCS) (pp. 893–902).
Li, Y.-J., & Liu, B. (2007). A normalized Levenshtein distance metric. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(6), 1091–1095.
Yang, A., Han, Y., Pan, Y., et al. (2017). Optimum surface roughness prediction for titanium alloy by adopting response surface methodology. Results in Physics, 7, 1046–1050.
Li, J., Huang, L., Zhou, Y., et al. (2017). Computation partitioning for mobile cloud computing in a big data environment. IEEE Transactions on Industrial Informatics, 13(4), 2009–2018.
Li, J., Yu, F. R., Deng, G., et al. (2017). Industrial internet: A survey on the enabling technologies, applications, and challenges. IEEE Communications Surveys & Tutorials, 19(3), 1504–1526.
Li, J., Deng, G., Luo, C., et al. (2016). A hybrid path planning method in unmanned air/ground vehicle (UAV/UGV) cooperative systems. IEEE Transactions on Vehicular Technology, 65(12), 9585–9596.
Li, J. Q., Li, W. L., Deng, G. Q., et al. (2016). Continuous-behavior and discrete-time combined control for linear induction motor-based urban rail transit. IEEE Transactions on Magnetics, 52(7), 1–4.
Cui, K., Yang, W., & Gou, H. (2017). Experimental research and finite element analysis on the dynamic characteristics of concrete steel bridges with multi-cracks. Journal of Vibroengineering, 19(6), 4198–4209.
Wei, W., Fan, X., Song, H., et al. (2016). Imperfect information dynamic stackelberg game based resource allocation using hidden Markov for cloud computing. IEEE Transactions on Services Computing, 99, 1–13.
Cui, K., & Zhao, T. T. (2017). Unsaturated dynamic constitutive model under cyclic loading. Cluster Computing, 20(4), 2869–2879.
Wei, W., Song, H., Li, W., et al. (2017). Gradient-driven parking navigation using a continuous information potential field based on wireless sensor network. Information Sciences, 408, 100–114.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Wang, D., Zhao, X. & Sun, Q. Novel Fault-Tolerant Decompression Method of Corrupted LZSS Files. Wireless Pers Commun 102, 2499–2518 (2018). https://doi.org/10.1007/s11277-018-5268-6
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11277-018-5268-6