Abstract
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.
Chapter PDF
Similar content being viewed by others
References
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ojansivu, V., Lepistö, L., Ilmoniemi, M., Heikkilä, J. (2011). Degradation Based Blind Image Quality Evaluation. In: Heyden, A., Kahl, F. (eds) Image Analysis. SCIA 2011. Lecture Notes in Computer Science, vol 6688. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21227-7_29
Download citation
DOI: https://doi.org/10.1007/978-3-642-21227-7_29
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21226-0
Online ISBN: 978-3-642-21227-7
eBook Packages: Computer ScienceComputer Science (R0)