Degradation Based Blind Image Quality Evaluation

  • Ville Ojansivu
  • Leena Lepistö
  • Martti Ilmoniemi
  • Janne Heikkilä
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6688)


In this paper, we propose a novel framework for blind image quality evaluation. Unlike the common image quality measures evaluating compression or transmission artifacts this approach analyzes the image properties common to non-ideal image acquisition such as blur, under or over exposure, saturation, and lack of meaningful information. In contrast to methods used for adjusting imaging parameters such as focus and gain this approach does not require any reference image. The proposed method uses seven image degradation features that are extracted and fed to a classifier that decides whether the image has good or bad quality. Most of the features are based on simple image statistics, but we also propose a new feature that proved to be reliable in scene invariant detection of strong blur. For the overall two-class image quality grading, we achieved ≈ 90 % accuracy by using the selected features and the classifier. The method was designed to be computationally efficient in order to enable real-time performance in embedded devices.


image artifacts blur exposure no-reference quality measurement 


  1. 1.
    Crete, F., Dolmiere, T., Ladret, P., Nicolas, M.: The blur effect: Perception and estimation with a new no-reference perceptual blur metric. In: Proc. SPIE, vol. 6492 (2007)Google Scholar
  2. 2.
    Erasmus, S.J., Smith, K.C.A.: An automatic focusing and astigmatism correction system for the sem and ctem. Journal of Microscopy 27, 185–199 (1982)CrossRefGoogle Scholar
  3. 3.
    Ferzli, R., Karam, L.J.: No-reference objective wavelet based noise immune image sharpness metric. In: IEEE International Conference on Image Processing, pp. 405–408 (2005)Google Scholar
  4. 4.
    Ferzli, R., Karam, L.J.: A no-reference objective image sharpness metric based on the notion of just noticeable blur (JNB). IEEE Trans. Image Processing 18(4), 717–728 (2009)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Ke, Y., Tang, X., Jing, F.: The design of high-level features for photo quality assessment. In: IEEE Conference on Computer Vision and Pattern Recognition, New York, NY, pp. 419–426 (June 2006)Google Scholar
  6. 6.
    Liu, R.T., Li, Z.R., Jia, J.Y.: Image partial blur detection and classification. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8 (2008)Google Scholar
  7. 7.
    Sheikh, H.R., Bovik, A.C., Cormack, L.: No-reference quality assessment using natural scene statistics: JPEG 2000. IEEE Trans. Image Processing 14(11), 1918–1927 (2005)CrossRefGoogle Scholar
  8. 8.
    Tsomko, E., Kim, H.J., Paik, J., Yeo, I.K.: Efficient method of detecting blurry images. Journal of Ubiquitous Convergence Technology 2(1), 27–39 (2008)Google Scholar
  9. 9.
    Varadarajan, S., Karam, L.J.: An improved perception-based no-reference objective image sharpness metric using iterative edge refinement. In: IEEE International Conference on Image Processing, pp. 401–404 (2008)Google Scholar
  10. 10.
    Zhu, X., Milanfar, P.: A no-reference sharpness metric sensitive to blur and noise. In: International Workshop on Quality of Multimedia Experience, pp. 64–69 (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Ville Ojansivu
    • 1
  • Leena Lepistö
    • 2
  • Martti Ilmoniemi
    • 2
  • Janne Heikkilä
    • 1
  1. 1.Machine Vision GroupUniversity of OuluFinland
  2. 2.Nokia CorporationTampereFinland

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