Skip to main content
Log in

A New Objective Image Quality Assessment Metric: For Color and Grayscale Images

  • 3DR Express
  • Published:
3D Research

Abstract

Image quality assessment (IQA) is a challenging task in digital image processing where the images get distorted in several situations. In recent years, various IQA measures have been developed to assess the quality of images in subjective and objective manner. Although the popular measures like mean square error (MSE) and peak signal to noise ratio (PSNR) work well for grayscale images, they fail to measure the exact difference between two color images on a pixel by pixel basis. To overcome this problem, we made a slight modification in the MSE and PSNR to present new measures mean absolute variance and fidelity ration (FR). Moreover, we formulate a constant ‘k’ of value 9.542425094 to establish a relationship between FR of color and grayscale images. The performance of the proposed metric is validated by comparing its results with state of art metrics against a same set of benchmark dataset. Though the proposed method involves simple mathematical calculations and no human visual system model is employed, the experimental analysis shows that FR is found to be highly effective and robust measure especially for continuous tone, discrete tone, bi-level and inverted images. This measure is highly useful for applications require exact IQA where a change of one-pixel value is also not desirable.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. A57 image quality database [Internet]. Available from http://vision.eng.shizuoka.ac.jp/mod/page/view.php?id=26. Accessed 11 Mar 2018.

  2. Bovik, A. C. (2013). Automatic prediction of perceptual image and video quality. Proceedings of the IEEE, 101, 2008–2024.

    Google Scholar 

  3. Chandler, D. M., & Hemami, S. S. (2007). VSNR: a wavelet-based visual signal-to-noise ratio for natural images. IEEE Transactions on Image Processing, 16, 2284–2298.

    Article  MathSciNet  Google Scholar 

  4. Color250 image database [Internet]. Available from http://www.cse.cuhk.edu.hk/leojia/projects/color2gray/index.html. Accessed 11 Mar 2018.

  5. Damera-Venkata, N., Kite, T. D., Geisler, W. S., Evans, B. L., & Bovik, A. C. (2009). Image quality assessment based on degradation model. IEEE Transactions on Image Processing, 9, 636–650.

    Article  Google Scholar 

  6. Elshaikh, M., Fadzil, M. F.M., Kamel, N., & Isa C. M. N. C. (2012). Weighted signal-to-noise ratio average routing metric for dynamic sequence distance vector routing protocol in mobile ad-hoc networks. In IEEE 8th international colloquium on signal processing and its applications (pp. 329–34).

  7. Eskicioglu, A. M., & Fisher, P. S. (1995). Image quality measures and their performance. IEEE Transactions on Communications, 43, 2959–2965.

    Article  Google Scholar 

  8. Gao, C., Panetta, K., & Agaian, S. (2013). No reference color image quality measures. In 2013 IEEE international conference on cybernetics (CYBCO), Lausanne (pp. 243–8).

  9. Gao, F., Wang, Yi, Li, P., Tan, M., Yu, J., & Zhu, Y. (2017). DeepSim: Deep similarity for image quality assessment. Neurocomputing, 257, 104–114.

    Article  Google Scholar 

  10. Gao, F., & Yu, J. (2016). Biologically inspired image quality assessment. Signal Processing, 124, 210–219.

    Article  Google Scholar 

  11. Gao, X., Lu, W., Tao, D., & Xuelong, L. (2012). Image quality assessment—a multiscale geometric analysis-based framework and examples. Handbook of Natural Computinghttps://doi.org/10.1007/978-3-540-92910-9_11.

    Article  Google Scholar 

  12. He, L., Gao, F., Hou, W., & Hao, L. (2014). Objective image quality assessment: a survey. International Journal of Computer Mathematics, 9, 2374–2388.

    Article  MathSciNet  Google Scholar 

  13. Hong, R., Pan, J., Hao, S., Wang, M., Xue, F., & Wu, X. (2014). Image quality assessment based on matching pursuit. Information Science (New York), 273, 196–211.

    Article  Google Scholar 

  14. [Internet 2018]. Available from https://www.researchgate.net/post/How_i_can_find_the_MSE_for_Color_Images. Accessed 12 June 2018.

  15. [Internet 2018]. Available from https://stackoverflow.com/questions/16264141/power-signal-noise-ratio-psnr-of-colored-jpeg-image. Accessed 12 June 2018.

  16. IRCCyN/IVC database [Internet]. Available from http://www2.irccyn.ec-nantes.fr/ivcdb/. Accessed 11 Mar 2018.

  17. Liu, A., Lin, W., & Narwaria, M. (2012). Image quality assessment based on gradient similarity. IEEE Transactions on Image Processing, 21, 1500–1512.

    Article  MathSciNet  Google Scholar 

  18. LIVE Image Quality Assessment Database [Internet]. Available from http://live.ece.utexas.edu/research/quality/subjective.htm. Accessed 11 Mar 2018.

  19. Ma, K., Zhao, T., Zeng, K., & Wang, Z. (2012). Objective quality assessment for color-to-gray image conversion. IEEE Transactions on Image Processing, 24, 4673–4685.

    Article  MathSciNet  Google Scholar 

  20. Moorthy, A. K., & Bovik, A. C. (2011). Blind image quality assessment: from natural scene statistics to perceptual quality. IEEE Transactions on Image Processing, 20, 3350–3364.

    Article  MathSciNet  Google Scholar 

  21. Rehman, A., & Wang, Z. (2012). Reduced-reference image quality assessment by structural similarity estimation. IEEE Transactions on Image Processing, 21, 3378–3389.

    Article  MathSciNet  Google Scholar 

  22. Reisenhofer, R., Bosse, S., Kutyniok, G., & Wiegand, T. (2018). A Haar wavelet-based perceptual similarity index for image quality assessment. Signal Processing: Image Communication, 1, 33–43.

    Google Scholar 

  23. Samani, K. P., SA (2015) TDMEC, a new measure for evaluating the image quality of color images acquired in vision systems. In 2015 IEEE international conference on technologies for practical robot applications (TePRA), Woburn (pp. 1–5).

  24. Sheikh, H. R., Bovik, A. C., & de Veciana, G. (2005). An information fidelity criterion for image quality assessment using natural scene statistics. IEEE Transactions on Image Processing, 14, 2117–2128.

    Article  Google Scholar 

  25. Thung, K. H., & Raveendran, P. (2009). A survey of image quality measures. In 2009 international conference for technical postgraduates (TECHPOS) (pp. 1–4).

  26. Uthayakumar, J., Vengattaraman, T., & Dhavachelvan, P. (2018). Discrete tone image dataset [Internet]. UCI machine learning repository. Available from https://archive.ics.uci.edu/ml/datasets/Discrete+Tone+Image+Dataset.

  27. Wang, Z., & Bovik, A. C. (2002). A universal image quality index. IEEE Signal Processing Letters, 9, 81–84.

    Article  Google Scholar 

  28. Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004). Image quality assessment: from error visibility to structural similarity. IEEE Transactions on Image Processing, 13, 600–612.

    Article  Google Scholar 

  29. Wang, Z., Simoncelli, E. P., & Bovik, A. C. (2003). Multiscale structural similarity for image quality assessment. In The thrity-seventh asilomar conference on signals, systems and computers (pp. 1398–402).

  30. Wang, Z., Wu, G., Sheikh, H., Simoncelli, E., Yang, E.-H., & Bovik, A. (2006). Quality-aware images. IEEE Transactions on Image Processing, 15, 1680–1689.

    Article  Google Scholar 

  31. Yuan, Y., Guo, Q., & Lu, X. (2015). Image quality assessment: A sparse learning way. Neurocomputing, 159, 227–241.

    Article  Google Scholar 

  32. Zhang, L., & Li, H. (2012). SR-SIM: A fast and high performance IQA index based on spectral residual. In 2012 19th IEEE international conference on image processing (ICIP) (pp. 1473–6).

  33. Zhang, L., Zhang, L., Mou, X., & Zhang, D. (2011). FSIM: A feature similarity index for image quality assessment. IEEE Transactions on Image Processing, 20, 2378–2386.

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Uthayakumar Jayasankar.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Jayasankar, U., Thirumal, V. & Ponnurangam, D. A New Objective Image Quality Assessment Metric: For Color and Grayscale Images. 3D Res 9, 28 (2018). https://doi.org/10.1007/s13319-018-0180-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s13319-018-0180-0

Keywords

Navigation