Skip to main content
SpringerLink
Log in
SpringerLink
Log in

A New Approach to Measuring Perceived Contrast for Complex Images

  • Conference paper
  • First Online:
Advances in Intelligent Systems and Computing III (CSIT 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 871))

Included in the following conference series:

Abstract

The problem of assessing the overall contrast of complex images is considered. The problem of increasing the accuracy of the quantitative evaluation of the perceived contrast for the complex multi-element images is being solved. This paper proposes a new approach to measuring the overall contrast of a complex image based on an estimate of the perceived contrast of the all pairs of elements of image (objects and background). A new approach to estimating the contrast of two elements of a complex image is proposed based on measuring the perceived contrast of a simple two-element image of these elements relative to the adaptation level for this simple image. The proposed approach is based on measuring the ratios between the values of mean absolute deviation of the brightness of simple image relative to the level of adaptation and the level of adaptation. A possible implementation of the proposed approach to measuring the perceived contrast for a weighted contrast kernel is considered. A new technique of measuring the perceived contrast of multi-element images for weighted contrast is proposed. The paper presents the results of research of the known no-reference metrics of contrast and proposed technique of measuring the perceived contrast of image for two groups of test images. The proposed technique of measuring the contrast enables to increase the accuracy of the estimated contrast for complex images.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Wang, Z., Bovik, A.C.: Modern image quality assessment. In: Synthesis Lectures on Image, Video, and Multimedia Processing, vol. 2, no. 1, pp. 1–156. Morgan and Claypool Publishers, New York (2006)

    Article  Google Scholar 

  2. Pratt, W.K.: Digital Image Processing: PIKS Inside, 3rd edn. Wiley, New York (2001)

    Book  Google Scholar 

  3. Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 3rd edn. Prentice Hall, Upper Saddle River (2010)

    Google Scholar 

  4. Nesteruk, V.F., Sokolova, V.A.: Questions of the theory of perception of subject images and a quantitative assessment of their contrast. Opt.-Electron. Ind. 5, 11–13 (1980)

    Google Scholar 

  5. Peli, E.: Contrast in complex images. J. Opt. Soc. Am. A 7(10), 2032–2040 (1990)

    Article  Google Scholar 

  6. Yelmanova, E., Romanyshyn, Y.: No-reference contrast metric for medical images. In: Proceedings of the 2017 IEEE 37th International Conference on Electronics and Nanotechnology (ELNANO), Kyiv, Ukraine, pp. 338–343 (2017)

    Google Scholar 

  7. http://sipi.usc.edu/database/database.php?volume=misc

  8. Yelmanov, S., Romanyshyn, Y.: Image contrast enhancement in automatic mode by nonlinear stretching. In: Proceedings of the 2018 XIV-th International Conference on Perspective Technologies and Methods in MEMS Design, Lviv, Ukraine, pp. 104–108 (2018)

    Google Scholar 

  9. Hassan, N., Akamatsu, N.: A new approach for contrast enhancement using sigmoid function. Int. Arab. J. Inf. Technol. 1(2), 221–225 (2004)

    Google Scholar 

  10. Hummel, R.: Image enhancement by histogram transformation. Comp. Graph. Image Process. 6, 184–195 (1977)

    Article  Google Scholar 

  11. Frei, W.: Image enhancement by histogram hyperbolization. Comput. Graph. Image Process. 6(3), 286–294 (1977)

    Article  Google Scholar 

  12. Kim, Y.T.: Contrast enhancement using brightness preserving bi-histogram equalization. IEEE Trans. Consum. Electron. 43(1), 1–8 (1997)

    Article  Google Scholar 

  13. Wang, Y., Chen, Q., Zhang, B.: Image enhancement based on equal area dualistic sub image histogram equalization method. IEEE Trans. Consum. Electron. 45(1), 68–75 (1999)

    Article  Google Scholar 

  14. Yelmanov, S., Romanyshyn, Y.: Automatic contrast enhancement of complex low-contrast images. In: Proceedings of IEEE 14th International Conference on TCSET 2018, Lviv-Slavske, Ukraine, pp. 952–957 (2018)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Special Design Office of Television Systems, Lviv, Ukraine

    Sergei Yelmanov

  2. Lviv Polytechnic National University, Lviv, Ukraine

    Yuriy Romanyshyn

  3. University of Warmia and Mazury, Olsztyn, Poland

    Yuriy Romanyshyn

Authors
  1. Sergei Yelmanov
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yuriy Romanyshyn
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sergei Yelmanov .

Editor information

Editors and Affiliations

  1. Lviv Polytechnic National University, Lviv, Ukraine

    Natalia Shakhovska

  2. Institute of Computer Science and Information Technologies, Lviv Polytechnic National University, Lviv, Ukraine

    Mykola O. Medykovskyy

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Yelmanov, S., Romanyshyn, Y. (2019). A New Approach to Measuring Perceived Contrast for Complex Images. In: Shakhovska, N., Medykovskyy, M. (eds) Advances in Intelligent Systems and Computing III. CSIT 2018. Advances in Intelligent Systems and Computing, vol 871. Springer, Cham. https://doi.org/10.1007/978-3-030-01069-0_7

Download citation

Publish with us

Policies and ethics

Search

Navigation