Skip to main content
SpringerLink
Log in
Book cover

International Conference on Agents and Artificial Intelligence

ICAART 2014: Agents and Artificial Intelligence pp 296–311Cite as

Full-Reference Predictive Modeling of Subjective Image Quality Assessment with ANFIS

  • Conference paper
  • First Online:

  • 672 Accesses

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8946))

Abstract

Digital images often undergo through various processing and distortions which subsequently impacts the perceived image quality. Predicting image quality can be a crucial step to tune certain parameters for designing more effective acquisition, transmission, and storage multimedia systems. With the huge number of images captured and exchanged everyday, automatic prediction of image quality that correlates well with human judgment is steadily gaining increased importance. In this paper, we investigate the performance of three combinations of objective metrics for image quality prediction with an adaptive neuro-fuzzy inference system (ANFIS). Images are processed to extract various attributes which are then used to build a predictive model to estimate a differential mean opinion score for different types of distortions. Using a publicly available and subjectively rated image database, the proposed method is evaluated and compared to individual metrics and an existing technique based on correlation and error measures. The results prove that the proposed method can be a promising approach for predicting subjective quality of images.

E.M. El-Alfy—On leave from the College of Engineering, Tanta University, Egypt.

This is a preview of subscription content, 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   39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
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

Learn about institutional subscriptions

References

  1. Balamurugan, P., Rajesh, R.: Greenery image and non-greenery image classification using adaptive neuro-fuzzy inference system. In: International Conference on Computational Intelligence and Multimedia Applications, 2007, vol. 3, pp. 431–435 (2007)

    Google Scholar 

  2. Bouzerdoum, A., Havstad, A., Beghdadi, A.: Image quality assessment using a neural network approach. In: Proceedings of the Fourth IEEE International Symposium on Signal Processing and Information Technology, pp. 330–333 (2004)

    Google Scholar 

  3. Chetouani, A., Beghdadi, A., Deriche, M.: Image distortion analysis and classification scheme using a neural approach. In: 2nd European Workshop on Visual Information Processing (EUVIP) 2010, pp. 183–186 (2010)

    Google Scholar 

  4. Damera-Venkata, N., Kite, T.D., Geisler, W.S., Evans, B.L., Bovik, A.C.: Image quality assessment based on a degradation model. IEEE Trans, Image Process. 9(4), 636–650 (2000)

    Article  Google Scholar 

  5. De, I., Sil, J.: No-reference quality prediction of distorted/decompressed images using ANFIS. In: International Conference on Computer Technology and Development, ICCTD 2009, vol. 2, pp. 90–94 (2009)

    Google Scholar 

  6. El-Alfy, E.S., Riaz, M.: Image quality assessment using ANFIS approach. In: Proceedings of the 6th International Conference on Agents and Artificial Intelligence (ICAART), vol. 1, pp. 169–177 (2014)

    Google Scholar 

  7. He, L., Gao, F., Hou, W., Hao, L.: Objective image quality assessment: a survey. Int. J. Comput. Math. 91(11), 1–15 (2013)

    MathSciNet  MATH  Google Scholar 

  8. Jang, J.S.: ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans. Syst. Man Cybern. 23(3), 665–685 (1993)

    Article  Google Scholar 

  9. Kaya, S., Milanova, M., Talburt, J., Tsou, B., Altynova, M.: Subjective image quality prediction based on neural network. In: Proceedings of the 16th International Conference on Information Quality (2011)

    Google Scholar 

  10. Khuntia, S.R., Panda, S.: ANFIS approach for SSSC controller design for the improvement of transient stability performance. Math. Comput. Model. 57(12), 289–300 (2013)

    Article  Google Scholar 

  11. Kudelka Jr, M.: Image quality assessment. In: Proceedings of Contributed Papers, WDS 2012 Part I. pp. 94–99 (2012)

    Google Scholar 

  12. Kung, C.H., Yang, W.S., Huang, C.Y., Kung, C.M.: Investigation of the image quality assessment using neural networks and structure similarity. In: Proceedings of the 3rd International Symposium Computer Science and Computational Technology (2010)

    Google Scholar 

  13. Lahoulou, A., Bouridane, A., Viennet, E., Haddadi, M.: Full-reference image quality metrics performance evaluation over image quality databases. Arab. J. Sci. Eng. 38(9), 2327–2356 (2013)

    Article  Google Scholar 

  14. Larson, E.C., Chandler, D.M.: Most apparent distortion: full-reference image quality assessment and the role of strategy. J. Electron. Imaging 19(1), 011006–011006 (2010)

    Article  Google Scholar 

  15. Li, C., Bovik, A.C., Wu, X.: Blind image quality assessment using a general regression neural network. IEEE Trans. Neural Networks 22(5), 793–799 (2011)

    Article  Google Scholar 

  16. Lin, W.: Jay Kuo, C.C.: Perceptual visual quality metrics: a survey. J. Vis. Commun. Image Represent. 22(4), 297–312 (2011)

    Article  Google Scholar 

  17. Sri Meena, R., Revathi, P., Reshma Begum, H.M., Singh, A.B.: Performance analysis of neural network and ANFIS in brain MR image classification. In: Patnaik, S., Yang, Y.-M. (eds.) Soft Computing Techniques in Vision Sci. SCI, vol. 395, pp. 101–113. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  18. Meharrar, A., Tioursi, M., Hatti, M., Stambouli, A.B.: A variable speed wind generator maximum power tracking based on adaptive neuro-fuzzy inference system. Expert Syst. Appl. 38(6), 7659–7664 (2011)

    Article  Google Scholar 

  19. Rehman, A., Wang, Z.: Reduced-reference image quality assessment by structural similarity estimation. IEEE Trans. Image Process. 21(8), 3378–3389 (2012)

    Article  MathSciNet  Google Scholar 

  20. Sheikh, H.R., Bovik, A.C.: Image information and visual quality. IEEE Trans. Image Process. 15(2), 430–444 (2006)

    Article  Google Scholar 

  21. Sheikh, H.R., Bovik, A.C., De Veciana, G.: An information fidelity criterion for image quality assessment using natural scene statistics. IEEE Trans. Image Process. 14(12), 2117–2128 (2005)

    Article  Google Scholar 

  22. Sheikh, H.R., Sabir, M.F., Bovik, A.C.: A statistical evaluation of recent full reference image quality assessment algorithms. IEEE Trans. Image Process. 15(11), 3440–3451 (2006)

    Article  Google Scholar 

  23. Wang, Z., Bovik, A.: Mean squared error: love it or leave it? a new look at signal fidelity measures. IEEE Signal Process. Mag. 26(1), 98–117 (2009)

    Article  Google Scholar 

  24. Wang, Z., Bovik, A.C.: A universal image quality index. IEEE Signal Process. Lett. 9(3), 81–84 (2002)

    Article  Google Scholar 

  25. Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004)

    Article  Google Scholar 

  26. Wee, C.Y., Paramesran, R., Mukundan, R., Jiang, X.: Image quality assessment by discrete orthogonal moments. Pattern Recogn. 43(12), 4055–4068 (2010)

    Article  MATH  Google Scholar 

  27. Yi, Y., Yu, X., Wang, L., Yang, Z.: Image quality assessment based on structural distortion and image definition. In: Proceedings of the International Conference on Computer Science and Software Engineering, 6, 253–256 (2008)

    Google Scholar 

  28. Zhang, F., Ma, L., Li, S., Ngan, K.N.: Practical image quality metric applied to image coding. IEEE Trans. Multimedia 13(4), 615–624 (2011)

    Article  Google Scholar 

  29. Zhu, X., Milanfar, P.: Automatic parameter selection for denoising algorithms using a no-reference measure of image content. IEEE Trans. Image Process. 19(12), 3116–3132 (2010)

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. College of Computer Sciences and Engineering, King Fahd University of Petroleum and Minerals, Dhahran, 31261, Saudi Arabia

    El-Sayed M. El-Alfy & Mohammed Rehan Riaz

Authors
  1. El-Sayed M. El-Alfy
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mohammed Rehan Riaz
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to El-Sayed M. El-Alfy .

Editor information

Editors and Affiliations

  1. LERIA - UFR Sciences, Angers, France

    Béatrice Duval

  2. Leiden University, Leiden, The Netherlands

    Jaap van den Herik

  3. LERIA - UFR Sciences, Angers, France

    Stephane Loiseau

  4. INSTICC, Polytechnic Institute of Setúbal, Setúbal, Portugal

    Joaquim Filipe

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

El-Alfy, ES.M., Riaz, M.R. (2015). Full-Reference Predictive Modeling of Subjective Image Quality Assessment with ANFIS. In: Duval, B., van den Herik, J., Loiseau, S., Filipe, J. (eds) Agents and Artificial Intelligence. ICAART 2014. Lecture Notes in Computer Science(), vol 8946. Springer, Cham. https://doi.org/10.1007/978-3-319-25210-0_18

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-25210-0_18

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-25209-4

  • Online ISBN: 978-3-319-25210-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation