Advertisement

A Byte Pattern Based Method for File Compression

  • José Luis Hernández-HernándezEmail author
  • Mario Hernández-Hernández
  • Sajad Sabzi
  • Mario Andrés Paredes-Valverde
  • Alejandro Fuentes Penna
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1124)

Abstract

This research presents a method to allows the data compression from a file containing any type of information by combining the pattern theory with the theory of data compression. This proposal can reduce the storage space of a file data from any kind of computer, platform or operating system installed on that computer. According to the fundamentals of patterns, a pattern is a regularity of bytes contained within a file with self-similarity characteristics; if this concept applies to data files, we find certain amounts of auto-similar or patterns repeated several times throughout the file; with a store data representation and being referenced, at a certain point data can be recovered from the original file without losing a single data, and consequently saving space on the hard disk.

In the search for various ways to compress data, led me to analyze and implement the proposed methodology in a beta mode compression software for Windows 10, which presents very compromising results.

Keywords

Patterns Data compression Tiles Mathematical pattern 

Notes

Acknowledgments

Authors are grateful to TecNM/Technological Institute of Chilpancingo, Autonomous University of Guerrero (UAGro), University of Mohaghegh Ardabili, University of Murcia and TecNM/CIIDET for supporting this work.

References

  1. 1.
    Akiyama, J.: Tile-makers and semi-tile-makers. Am. Math. Mon. 114(7), 602–609 (2007)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Bachu, S., Chari, K.M.: A review on motion estimation in video compression. In: 2015 International Conference on Signal Processing and Communication Engineering Systems, pp. 250–256. IEEE (2015)Google Scholar
  3. 3.
    Belchor, P.M., et al.: Use of fractals channels to improve a proton exchange membrane fuel cell performance. J. Energy Power Eng. 9, 727–730 (2015)Google Scholar
  4. 4.
    Bellomo, N., Bellouquid, A., Tao, Y., Winkler, M.: Toward a mathematical theory of Keller-Segel models of pattern formation in biological tissues. Math. Models Meth. Appl. Sci. 25(09), 1663–1763 (2015)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Bentley, J., McIlroy, D.: Data compression using long common strings. In: Proceedings DCC 1999 Data Compression Conference (Cat. No. PR00096), pp. 287–295. IEEE (1999)Google Scholar
  6. 6.
    Bentley, J., McIlroy, D.: Data compression with long repeated strings. Inf. Sci. 135(1–2), 1–11 (2001)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Kuhn, M., Kunkel, J.M., Ludwig, T.: Data compression for climate data. Supercomput. Front. Innov. 3(1), 75–94 (2016)Google Scholar
  8. 8.
    Larsson, N.J.: Structures of String Matching and Data Compression. Lund University, Sweden (1999)Google Scholar
  9. 9.
    Lippert, L., Gross, M.H., Kurmann, C.: Compression domain volume rendering for distributed environments. Comput. Graph. Forum 16, C95–C107 (1997)CrossRefGoogle Scholar
  10. 10.
    Long, P.M., Natsev, A.I., Vitter, J.S.: Text compression via alphabet re-representation. Neural Netw. 12(4–5), 755–765 (1999)CrossRefGoogle Scholar
  11. 11.
    Makkar, A., Singh, G., Narula, R.: Improving LZW compression 1 (2012)Google Scholar
  12. 12.
    Müldner, T., Leighton, G., Diamond, J.: Using XML compression for WWW communication. In: Proceedings of the IADIS WWW/Internet 2005 Conference (2005)Google Scholar
  13. 13.
    RarLab, WinRar: software system for compress files (2019). http://www.win-rar.com/rarproducts.html. Accessed 08 Aug 2019
  14. 14.
    Reghbati, H.K.: Special feature an overview of data compression techniques. Computer 14(4), 71–75 (1981)CrossRefGoogle Scholar
  15. 15.
    Reghizzi, S.C., Pradella, M.: Tile rewriting grammars and picture languages. Theor. Comput. Sci. 340(2), 257–272 (2005)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Santaolaya, S.R.: Ambiente de Desarrollo para la Programación Visual de Interfaces de Usuario para Monitoreo de Procesos en Línea. Ph.D. thesis, Centro Nacional de Investigación y Desarrollo Tecnológico (CENIDET) (1995)Google Scholar
  17. 17.
    Sayood, K.: Introduction to Data Compression. Morgan Kaufmann, Burlington (2017)zbMATHGoogle Scholar
  18. 18.
    Sikora, T.: MPEG digital video coding standards. In: Compressed Video over Networks, pp. 45–88. CRC Press (2018)Google Scholar
  19. 19.
    Soria, F.G., et al.: Sistemas evolutivos. Boletín de Política Informática. México (1986)Google Scholar
  20. 20.
    Stamps, A.E.: Fractals, skylines, nature and beauty. Landsc. Urban Plan. 60(3), 163–184 (2002)CrossRefGoogle Scholar
  21. 21.
    SubhamastanRao, T., Soujanya, M., Hemalatha, T., Revathi, T.: Simultaneous data compression and encryption. Int. J. Comput. Sci. Inf. Technol. 2(5), 2369–2374 (2011)Google Scholar
  22. 22.
    WinZip: Program of compression for windows (2019). http://www.winzip.com/ru/prodpagewz.htm. Accessed 08 Aug 2019
  23. 23.
    Wu, H., Chen, Q., Yachida, M.: Face detection from color images using a fuzzy pattern matching method. IEEE Trans. Pattern Anal. Mach. Intell. 21(6), 557–563 (1999)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.TecNM/Technological Institute of ChilpancingoChilpancingoMexico
  2. 2.Autonomous University of GuerreroChilpancingoMexico
  3. 3.University of Mohaghegh ArdabiliArdabilIran
  4. 4.University of MurciaMurciaSpain
  5. 5.TecNM/CIIDETQuerétaroMexico

Personalised recommendations