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Image Quality Improvements Using Quantization Matrices of Standard Digital Cameras in DCT Based Compressor

  • Mahendra M. DixitEmail author
  • C. Vijaya
Original Contribution

Abstract

In the field of image compression, loads of scope exist for research. This work facilitates the platform to investigate and divulge the evidence of usage of vector quantization process in unique, prominent and renowned digital cameras of the present day. The work encompasses discrete cosine transform (DCT) based image compression technique using vector quantization of corresponding digital cameras, applied to standard test images. The luminance and chrominance quantization matrices of two digital cameras, namely Canon PowerShot A700 (Superfine) and Fuji FinePix A700 (Fine), are selected, which are investigated and verified as quantization level of 50. The proposed technique comprises not only one level of quantization; however, the vector quantization is variable from level 10 to 75. Both qualitative and quantitative analysis are carried out to verify the functionality and applicability of the algorithm. In addition, the functional verification of the said technique is carried out on Raspberry Pi low-cost embedded platform.

Keywords

DCT Vector quantization Image compression Digital cameras Raspberry Pi 

Notes

References

  1. 1.
    I.M. Pu, Fundamental Data Compression, ISBN-13:978-0-7506-6310-6, ISBN-10:0-7506-6310-3, British Library Cataloguing in Publication Data and Library of Congress Cataloguing in Publication Data, Butterworth–Heinemann Publications Copyright © 2006. http://books.elsevier.com
  2. 2.
    A. Skodras, C. Christopoulos, T. Ebrahimi, The JPEG 2000 still image compression standard. IEEE Signal Process. Mag. 18(5), 36–58 (2001)CrossRefGoogle Scholar
  3. 3.
    J.M. Martínez, ed., The MPEG Standard, ISO/MPEG N4674, Overview of the MPEG–7 standard, Version 6.0, MPEG Requirements Group, Jeju, (2002)Google Scholar
  4. 4.
    J.D. Kornblum, Using JPEG quantization tables to identify imagery processed by software. Digit. Investig. 5(1), S21–S25 (2008).  https://doi.org/10.1016/j.diin.2008.05.004 MathSciNetCrossRefGoogle Scholar
  5. 5.
    K. Sayood, Introduction to Data Compression, 3rd edn. (Morgan Kaufmann-Elsevier Publications, Burlington, 2017). ISBN 13: 978-0-12-620862-7Google Scholar
  6. 6.
    M.M. Dixit, P. kumar, Comparative Analysis of Variable Quantization DCT and Variable Rank Matrix SVD Algorithms for Image Compression Applications, 2010 IEEE International Conference on Computational Intelligence and Computing Research.  https://doi.org/10.1109/iccic.2010.5705879, IEEE Xplore
  7. 7.
  8. 8.
    M. Jeong, J.H. Kang, Y.S. Mun, D.H. Jung, JPEG Quantization Table Design for Photos with Face in Wireless Handset, 5th Pacific Rim Conference on Multimedia Tokyo, Japan (PCM 2004) Proceedings Part–III, Springer, Berlin , 2004, LNCS 3333, ISBN 3-540-23985-5, pp. 68–688.  https://doi.org/10.1007/978-3-540-30543-9_85 CrossRefGoogle Scholar
  9. 9.
    H. Farid, Digital Image Ballistics from JPEG Quantization. Computer Science, Dartmouth College, Tech Rep TR2006–583, pp. 1–6Google Scholar
  10. 10.
    A. Cheddad, J. Condell, K. Curran, P. Mc Kevitt, A review paper digital image steganography survey and analysis of current methods. Signal Process. 90, 727–752 (2010).  https://doi.org/10.1016/j.sigpro.2009.08.010 CrossRefzbMATHGoogle Scholar
  11. 11.
    B. Mahdian, S. Saic, A bibliography on blind methods for identifying image forgery. Signal Process. Image Commun. (2010).  https://doi.org/10.1016/j.image.2010.05.003 CrossRefGoogle Scholar
  12. 12.
    S. Braci, C. Delpha, R. Boyer, How quantization based schemes can be used in image steganography context. Signal Process. Image Commun. 26(8), 567–576 (2011).  https://doi.org/10.1016/j.image.2011.07.006 CrossRefGoogle Scholar
  13. 13.
    R. Zhang, R.D. Wang, In-camera JPEG Compression Detection for Doubly Compressed Images, Multimedia Tools and Applications (Springer, New York, 2014), pp. 1–20Google Scholar
  14. 14.
    S. Ye, Q. Sun and E.C. Chang, Detecting Digital Image Forgeries by Measuring Inconsistencies of Blocking Artefact, ISBN:1-4244-1017-7/07, IEEE ICME 2007 pp. 12–15Google Scholar
  15. 15.
    F. Ernawan, N.A. Abu, N. Suryana, Integrating a smooth Psychovisual threshold into an adaptive JPEG image compression. J Comput. Acad. Publ. 9(3), 644–653 (2014).  https://doi.org/10.4304/jcp.9.3.644-653 CrossRefGoogle Scholar
  16. 16.
    M.M. Dixit, C. Vijaya , Q-Factor based Modified Adaptable Vector Quantization Techniques for DCT based Image Compression and DSP Implementation, In: 6th international conference on innovations in electronics and communication engineering, Hyderabad, Proceedings will be published in Springer Lecture Notes in Networks and Systems 2017Google Scholar
  17. 17.
    M.M. Dixit, C. Vijaya , Effects of Hybrid SVD–DCT based Image Compression Scheme using Variable Rank Matrix and Modified Vector Quantization, In: 5th international conference on innovations in computer science and engineering, Hyderabad, Proceedings will be published in Springer Lecture Notes in Networks and Systems 2017Google Scholar
  18. 18.
    M.M. Dixit, C. Vijaya, DSP implementation of modified variable vector quantization based image compression using DCT and synthesis on FPGA. Int. J. Inform. Technol. (2018).  https://doi.org/10.1007/s41870-018-0162-8 CrossRefGoogle Scholar
  19. 19.
    The Matlab (R2014b). www.mathworks.com. Accessed 15 Feb 2019

Copyright information

© The Institution of Engineers (India) 2019

Authors and Affiliations

  1. 1.Department of E&CE, SDMCETDharwadIndia

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