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

  • El-Sayed M. El-AlfyEmail author
  • Mohammed Rehan Riaz
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8946)


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.


Image quality assessment Adaptive neuro-fuzzy inference system ANFIS Differential mean opinion score Human visual system Subjective assessment Objective assessment 


  1. 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. 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. 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. 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)CrossRefGoogle Scholar
  5. 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. 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. 7.
    He, L., Gao, F., Hou, W., Hao, L.: Objective image quality assessment: a survey. Int. J. Comput. Math. 91(11), 1–15 (2013)MathSciNetzbMATHGoogle Scholar
  8. 8.
    Jang, J.S.: ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans. Syst. Man Cybern. 23(3), 665–685 (1993)CrossRefGoogle Scholar
  9. 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. 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)CrossRefGoogle Scholar
  11. 11.
    Kudelka Jr, M.: Image quality assessment. In: Proceedings of Contributed Papers, WDS 2012 Part I. pp. 94–99 (2012)Google Scholar
  12. 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. 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)CrossRefGoogle Scholar
  14. 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)CrossRefGoogle Scholar
  15. 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)CrossRefGoogle Scholar
  16. 16.
    Lin, W.: Jay Kuo, C.C.: Perceptual visual quality metrics: a survey. J. Vis. Commun. Image Represent. 22(4), 297–312 (2011)CrossRefGoogle Scholar
  17. 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) CrossRefGoogle Scholar
  18. 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)CrossRefGoogle Scholar
  19. 19.
    Rehman, A., Wang, Z.: Reduced-reference image quality assessment by structural similarity estimation. IEEE Trans. Image Process. 21(8), 3378–3389 (2012)MathSciNetCrossRefGoogle Scholar
  20. 20.
    Sheikh, H.R., Bovik, A.C.: Image information and visual quality. IEEE Trans. Image Process. 15(2), 430–444 (2006)CrossRefGoogle Scholar
  21. 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)CrossRefGoogle Scholar
  22. 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)CrossRefGoogle Scholar
  23. 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)CrossRefGoogle Scholar
  24. 24.
    Wang, Z., Bovik, A.C.: A universal image quality index. IEEE Signal Process. Lett. 9(3), 81–84 (2002)CrossRefGoogle Scholar
  25. 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)CrossRefGoogle Scholar
  26. 26.
    Wee, C.Y., Paramesran, R., Mukundan, R., Jiang, X.: Image quality assessment by discrete orthogonal moments. Pattern Recogn. 43(12), 4055–4068 (2010)CrossRefzbMATHGoogle Scholar
  27. 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. 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)CrossRefGoogle Scholar
  29. 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)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.College of Computer Sciences and EngineeringKing Fahd University of Petroleum and MineralsDhahranSaudi Arabia

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