Parameterization of the nonlinear Greham predictor for digital image compression
M.V. Gashnikov

 

Samara State Aerospace University, Samara, Russia

Full text of article: Russian language.

Abstract:
Parameterization of the nonlinear Greham predictor is performed for a digital image compression method based on the differential pulse code modulation. The predictor automatically selects different methods for calculating each pixel in the image based on the availability and intensity of that pixel contour. A fast learning procedure that optimizes the prediction parameters is performed before the actual compression. In the course of optimization, the minimum sum of absolute values of prediction errors is provided. For this purpose, a recursive procedure is used, whose computational complexity is independent of the image size. The estimation of the computational complexity of the proposed predictor is conducted. To study the predictors computational experiments are carried out on real images. A gain the proposed predictor offers in terms of the root mean square error when compared with the prototypes is demonstrated. In addition, a gain that the compression method based on the differential pulse code modulation with the proposed predictor has over the JPEG compression method in terms of the maximum error is demonstrated.

Keywords:
digital image compression, Greham predictor, quantization, Max scale, DPCM, mean square error, maximum error.

Citation:
Gashnikov MV. Parameterization of the nonlinear Greham predictor for digital image compression. Computer Optics 2016; 40(2): 225-31. DOI: 10.18287/2412-6179-2016-40-2-225-231.

References:

  1. Salomon D. Data Compression. The Complete Reference. Springer-Verlag, 4ed; 2007.
  2. Vatolin D, Ratushnyak A, Smirnov M, Yukin V. Data compression methods. Archive program architecture, image and video compression[In Russian]. Moscow: DIALOG-MIFI; 2002.
  3. Pratt W. Digital image processing. 4ed. Wiley; 2007.
  4. Soifer VA, Chernov AV, Chernov VM, Chicheva MA, Fursov VA, Gashnikov MV, Glumov NI, Ilyasova NY, Khramov AG, Korepanov AO, Kupriyanov AV, Myasnikov EV, Myasnikov VV, Popov SB, Sergeyev VV. Computer Image Processing, Part II: Methods and algorithms. Ed by Soifer VA. VDM Verlag; 2010.
  5. Woods E, Gonzalez R. Digital Image Processing. 3ed. Prentice Hall; 2007.
  6. Shovengerdt R. Remote sensing. Models and methods for image processing [In Russian]. Moscow: Tehnosfera; 2010.
  7. Chang C. Hyperspectral Data Processing: Algorithm Design and Analysis. Wiley Press; 2013.
  8. Borengasser M, Hungate W, Watkins R. Hyperspectral Remote Sensing – Principles and Applications. CRC Press; 2004.
  9. Chang C. Hyperspectral data exploitation: theory and applications. Wiley-Interscience; 2007.
  10. Benz U, Hofmann P, Willhauck G, Lingenfelder I, Heynen M. Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information. ISPRS Journal of Photogrammetry and Remote Sensing 2004; 58(3): 239-258.
  11. Anderson J, Hardy E, Roach J, Witme R. A land use and land cover classification system for use with remote sensor data. US Government Printing Office; 1976.
  12. Gashnikov MV, Glumov NI, Myasnikov VV, Chernov AV, Ivanova EV. Regional Geographic Information Systems for Gas Network Monitoring. Pattern Recognition and Image Analysis 2015; 25(3): 418-422. DOI: 10.1134/S1054661815030062.
  13. Wallace G. The JPEG Still Picture Compression Standard. Communications of the ACM 1991; 34(4): 30-44.
  14. Efimov VM, Kolesnikov AN. Estimation of efficiency of hierarchical and sequential compression algorithms of grayscale images without losses. Pattern Recognition and Image Analysis: new information technologies processing 1997; 1: 157-161.
  15. Netravali N, Limb J. Picture coding: A review. IEEE proceeding 1980; 68(3): 366-406. DOI: 10.1109/PROC.1980.11647.
  16. Gashnikov MV, Glumov NI, Sergeyev VV. Adaptive interpolation algorithm for hierarchical image compression [In Russian]. Computer Optics 2002; 23: 89-93.
  17. Lin S, Costello D. Error Control Coding: Fundamentals and Applications, second edition. New Jersey: Prentice-Hall, inc. Englewood Cliffs; 2004.
  18. Gashnikov MV, Glumov NI. Hierarchical grid interpolation for hyperspectral image compression. Computer Optics 2014; 38(1): 87-93.
  19. Gashnikov MV, Glumov NI. Hierarchical GRID Interpolation under Hyperspectral Images Compression. Optical Memory and Neural Networks (Information Optics) 2014; 23(4): 246-253.
  20. Chang C. Hyperspectral imaging: techniques for spectral detection and classification. Springer; 2003.
  21. Chang C, Chiang S. Anomaly detection and classification for hyperspectral imagery. IEEE Transactions on Geoscience and Remote Sensing 2002; 40(6): 1314-1325.
  22. Gashnikov MV, Glumov NI. Hyperspectral images repository using a hierarchical compression. 23-rd International Conference on Computer Graphics, Visualization and Computer Vision proceeding 2015; 1-4. ISBN 978-80-86943-67-1. ISSN 2464–4617.
  23. Gashnikov MV, Glumov NI. Hierarchical compression for hyperspectral image storage. Computer Optics 2014; 38(3): 482-488.
  24. Waterloo Grey Set. University of Waterloo Fractal coding and analysis group: Mayer Gregory Image Repository; 2009. Source: <http://links.uwaterloo.ca/Repository.htm/>.

© 2009, IPSI RAS
Institution of Russian Academy of Sciences, Image Processing Systems Institute of RAS, Russia, 443001, Samara, Molodogvardeyskaya Street 151; e-mail: ko@smr.ru; Phones: +7 (846 2) 332-56-22, Fax: +7 (846 2) 332-56-20