Using Pre-Determined Patterns to Analyze the Common Behavior of Compressed Data and Their Compressibility Apeal

  • Abstract
  • Keywords
  • References
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  • Abstract

    This paper studies the behavior of compressed/uncompressed data on predetermined binary patterns. These patterns were generated according to specific criteria to ensure that they represent binary files. Each pattern is structurally unique. This study shows that all compressed data behave almost similarly when analyzing predetermined patterns. They all follow a curve similar to that of a skewed normal distribution. The uncompressed data, on the other hand, behave differently. Each file of uncompressed data plots its own curve without a specific shape. The paper confirms the side effect of these patterns, and the fact that they can be used to measure the compressibility appeal of compressed data.



  • Keywords

    Compressed Data, Uncompressed Data, Patterns, Compressibility, Randomness.

  • References

      [1] I. Bauermann and E. Steinbach, “Further lossless compression of Jpeg Images,” in In Proceedings of PCS 2004 – Picture Coding Symposium, CA, 2004.

      [2] N. Ponomarenko, K. Egiazarian, V. Lukin, and J. Astola, “Additional lossless compression of JPEG images,” in Image and Signal Processing and Analysis, 2005. ISPA 2005. Proceedings of the 4th International Symposium on, 2005, pp. 117–120.

      [3] M. Stirner and G. Seelmann, “Improved redundancy reduction for JPEG files,” in Proc. Picture Coding Symposium (PCS 2007), 2007.

      [4] I. Matsuda, Y. Nomoto, K. Wakabayashi, and S. Itoh, “Lossless re-encoding of JPEG images using block-adaptive intra prediction,” in Signal Processing Conference, 2008 16th European, 2008, pp. 1–5.

      [5] I. Matsuda, K. Wakabayashi, Y. Ikeda, and S. Itoh, “A lossless re-encoding scheme for MPEG-1 video,” in Signal Processing Conference, 2009 17th European, 2009, pp. 1834–1838.

      [6] M. Hasan, K. M. Nur, and H. Bin Shakur, “An Improved JPEG Image Compression Technique based on Selective Quantization,” Int. J. Comput. Appl., vol. 55, no. 3, 2012.

      [7] D. Salomon, A guide to data compression methods. Springer Science & Business Media, 2013.

      [8] B. Waggoner, Compression for great video and audio: master tips and common sense. Taylor & Francis, 2010.

      [9] W. Chang, B. Fang, X. Yun, S. Wang, X. Yu, and M. Ethodology, “Randomness Testing of Compressed Data,” vol. 2, no. 1, pp. 44–52, 2010.

      [10] W. Chang, X. Yun, N. Li, and X. Bao, “Investigating Randomness of the LZSS Compression Algorithm,” in Computer Science & Service System (CSSS), 2012 International Conference on, 2012, pp. 2001–2006.

      [11] V. V. Kamal A. Al-Khayyat, Imad F. Al-Shaikhli, “ON THE RANDOMNESS OF NON-PARAMETRIC RANDOMNESS TESTS AND THEIR STATISTICAL,” Bull. Electr. Eng. Informatics Univ. Ahmad Dahlan., vol. Vol 7, No, p. 63~69, 2018.

      [12] D. Salomon, Variable-length codes for data compression. Springer Science & Business Media, 2007.

      [13] M. J. Weinberger, G. Seroussi, and G. Sapiro, “LOCO-I: A low complexity, context-based, lossless image compression algorithm,” in Data Compression Conference, 1996. DCC’96. Proceedings, 1996, pp. 140–149.

      [14] M. J. Weinberger and G. Seroussi, “From LOCO-I to the JPEG-LS Standard,” 1999.

      [15] M. J. Weinberger, G. Seroussi, and G. Sapiro, “The LOCO-I lossless image compression algorithm: Principles and standandization into JPEG-LS,” IEEE Trans. Image Process., vol. 9, no. 8, pp. 1309–1326, 2000.

      [16] D. Tabuman and M. Marcellin, “JPEG2000: Image Compression Fundamentals, Standards and Parctice.” Norwell, MA: Kluwer, 2002.




Article ID: 13905
DOI: 10.14419/ijet.v7i2.34.13905

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