Statistical encoding for image compression based on hierarchical grid interpolation
Gashnikov M.V.

 

Samara National Research University, Samara, Russia

Full text of article: Russian language.

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Abstract:
Algorithms of statistical encoding for image compression are investigated. An approach is proposed to increase the efficiency of variable-length codes when compressing images with losses. An algorithm of statistical encoding is developed for use as part of image compression methods that encode a de-correlated signal with an uneven probability distribution. An experimental comparison of the proposed algorithm with the algorithms ZIP and ARJ is performed while encoding the specific data of the hierarchical compression method. In addition, an experimental comparison of the hierarchical method of image compression, including the developed coding algorithm, with the JPEG method and the method based on the wavelet transform is carried out.

Keywords:
image compression, statistical encoding, variable length codes, quantization, entropy, compressed data size.

Citation:
Gashnikov MV. Statistical encoding for image compression based on hierarchical grid interpolation. Computer Optics 2017; 41(6): 905-912. DOI: 10.18287/2412-6179-2017-41-6-905-912.

References:

  1. Schowengerdt RA. Remote sensing: models and methods for image processing. 3th ed. Burlington, San Diego: Academic Press; 2007. ISBN 978-0-12-369407-2.
  2. Chang Ch-I. Hyperspectral data processing: Algorithm design and analysis. Hoboken, NJ: A John Wiley & Sons, Inc; 2013. ISBN: 978-0-471-69056-6.
  3. Borengasser M, Hungate WS, Watkins R. Hyperspectral remote sensing: Principles and applications. Boca Raton, London, New York: CRC Press; 2007. ISBN: 978-1-56670-654-4.
  4. Gonzalez RC, Woods RE. Digital image processing. 3th ed. Upper Saddle River, NJ: Prentice Hall; 2007. ISBN: 978-0-13-168728-8.
  5. Pratt WK. Digital image processing. 4th ed. Hoboken, NJ: John Wiley & Sons, Inc; 2007. ISBN: 978-0-471-76777-0.
  6. Sayood K. Introduction to data compression. 4th ed. Waltham, MA: Morgan Kaufmann; 2012. ISBN: 978-0-12-415796-5.
  7. Salomon D. Data compression: The complete reference. 4th ed. London: Springer-Verlag; 2007. ISBN: 978-1-84628-602-5.
  8. Soifer VA, ed. Computer image processing, Part II: Methods and algorithms. VDM Verlag Dr Müller; 2010. ISBN: 978-3-6391-7545-5.
  9. Plonka G, Tasche M. Fast and numerically stable algorithms for discrete cosine transforms. Linear Algebra and its Applications 2005; 394: 309-345. DOI: 10.1016/j.laa.2004.07.015.
  10. Wallace GK. The JPEG still picture compression standard. Communications of the ACM 1991; 34(4): 30-44. DOI: 10.1145/103085.103089.
  11. Gupta V, Sharma V, Kumar A. Enhanced image compression using wavelets. International Journal of Research in Engineering and Science (IJRES) 2014; 2(5): 55-62.
  12. Li J. Image compression: The mathematics of JPEG-2000. Modern Signal Processing 2003; 46: 185-221.
  13. Gashnikov MV, Glumov NI, Sergeyev VV. Compression method for real-time systems of remote sensing. ICPR 2000; 3: 232-235. DOI: 10.1109/ICPR.2000.903527.
  14. Gashnikov MV, Glumov NI. Development and investigation of a hierarchical compression algorithm for storing hyperspectral images. Opt Mem Neural Networks 2016; 25(3): 168-179. DOI: 10.3103/S1060992X16030024.
  15. Ziv J, Lempel A. А universal algorithm for sequential compression. IEEE Trans Inf Theory 1977; 23(3): 337-343. DOI: 10.1109/TIT.1977.1055714.
  16. Huffman D. A method for the construction of minimum-redundancy codes. Proc IRE 1952; 40(9): 1098-1101. DOI: 10.1109/JRPROC.1952.273898.
  17. Written IH, Neal RM, Cleary JG. Arithmetic coding for data compression. Communication of the ACM 1987; 30(6): 520-540. DOI: 10.1145/214762.214771.
  18. Gashnikov MV, Glumov NI. Onboard processing of hyperspectral data in the remote sensing systems based on hierarchical compression [In Russian]. Computer Optics 2016; 40(4): 543-551. DOI: 10.18287/2412-6179-2016-40-4-543-551.
  19. Gashnikov MV, Glumov NI. Hierarchical GRID Interpolation under Hyperspectral Images Compression. Opt Mem Neural Networks 2014; 23(4): 246-253. DOI: 10.3103/S1060992X14040031.
  20. LuraTech: A business of Foxit software. Source: <http://www.luratech.com>.
  21. Vatolin D, Moskvin A, Petrov O, Titarenko A. MSU JPEG 2000 Image Codecs Comparison. Source: <http://compression.ru/video/codec_comparison/pdf/jpeg2000_codec_comparison_en.pdf>; 2005.
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