Advertisement

Multimedia Tools and Applications

, Volume 74, Issue 17, pp 7355–7378 | Cite as

Objective blur assessment based on contraction errors of local contrast maps

  • David Boon Liang Bong
  • Bee Ee Khoo
Article

Abstract

Blur distortion appears in multimedia content due to acquisition, compression or transmission errors. In this paper, a method is proposed to predict blur severity based on the contraction errors of local contrast maps. The proposed method is developed from the observation that histogram distribution of natural image would contract according to the degree of blur distortion. In order to quantify the level of contraction, an efficient method of determining local contrast boundaries is used. The upper and lower bounds of local histogram distribution are defined for the original image, and outlying points beyond these bounds are used to form the local contrast map. For the corresponding patch of a blur image, the same values of upper and lower bounds are used and the local contrast map for the blur image could be produced. Total difference between local contrast maps of the original and blur images is the contraction errors which are used to derive the blur score. The proposed method has advantages in terms of computation efficiency, and is performed in the spatial domain without the need of data transformation, conversion or filtering. In addition, prior training is not required at all for the model. Implementation of the proposed method as a multimedia tool is useful for estimating blur severity in multimedia content. The performance of the proposed method is verified by using three different blur databases and compared to popular state-of-the-art methods. Experiment results show that the proposed blur metric has high correlation with human perception of blurriness.

Keywords

Objective blur assessment Contrast maps Contraction errors Histogram distribution Spatial domain 

References

  1. 1.
    Chen MJ, Bovik A (2011) No-reference image blur assessment using multiscale gradient. EURASIP J Image Video Process 2011(3):1–11. doi: 10.1186/1687-5281-2011-3 zbMATHCrossRefGoogle Scholar
  2. 2.
    Ciancio A, da Costa ALNT, da Silva EAB, Said A, Samadani R, Obrador P (2009) Objective no-reference image blur metric based on local phase coherence. Electron Lett 45(23):1162–1163. doi: 10.1049/el.2009.1800 CrossRefGoogle Scholar
  3. 3.
    Cohen E, Yitzhaky Y (2010) No-reference assessment of blur and noise impacts on image quality. Signal Image Video Process 4(3):289–302. doi: 10.1007/s11760-009-0117-4 CrossRefGoogle Scholar
  4. 4.
    Ferzli R, Karam LJ (2009) A no-reference objective image sharpness metric based on the notion of just noticeable blur (JNB). IEEE Trans Image Process 18(4):717–728. doi: 10.1109/TIP.2008.2011760 MathSciNetCrossRefGoogle Scholar
  5. 5.
    Geisler WS (2008) Visual perception and the statistical properties of natural scenes. Annu Rev Psychol 59(1):167–192. doi: 10.1146/annurev.psych.58.110405.085632 MathSciNetCrossRefGoogle Scholar
  6. 6.
    Han HS, Kim DO, Park RH (2009) Structural information-based image quality assessment using LU factorization. IEEE Trans Consumer Electron 55(1):165–171. doi: 10.1109/TCE.2009.4814430 CrossRefGoogle Scholar
  7. 7.
    Haun AM, Peli E (2013) Is image quality a function of contrast perception? Proc SPIE 8651. doi: 10.1117/12.2008620
  8. 8.
    Jain A, Bhateja V (2011) A full-reference image quality metric for objective evaluation in spatial domain. Proc Int Conf Commun and Ind Appl. 1–5. doi: 10.1109/ICCIndA.2011.614666
  9. 9.
    Larson EC, Chandler DM (2010) Most apparent distortion: full-reference image quality assessment and the role of strategy. J Electron Imaging 19(1). doi: 10.1117/1.3267105
  10. 10.
    Marziliano P, Dufaux F, Winkler S, Ebrahimi T (2004) Perceptual blur and ringing metrics: applications to JPEG2000. Signal Proccess: Image Commun 19:163–172Google Scholar
  11. 11.
    Mittal A, Moorthy AK, Bovik AC (2012) No-reference image quality assessment in the spatial domain. IEEE Trans Image Process 21(12):4695–4708. doi: 10.1109/TIP.2012.2214050 MathSciNetCrossRefGoogle Scholar
  12. 12.
    Narvekar ND, Karam LJ (2011) A no-reference image blur metric based on the cumulative probability of blur detection (CPBD). IEEE Trans Image Process 20(9):2678–2683. doi: 10.1109/TIP.2011.2131660 MathSciNetCrossRefGoogle Scholar
  13. 13.
    Ponomarenko N, Lukin V, Zelensky A, Egiazarian K, Carli M, Battisti F (2009) TID2008 - a database for evaluation of full-reference visual quality assessment metrics. Adv Mod Radioelectron 10(10):30–45Google Scholar
  14. 14.
    Saad MA, Bovik AC, Charrier C (2010) A DCT statistics-based blind image quality index. IEEE Signal Process Lett 17(6):583–586. doi: 10.1109/LSP.2010.2045550 CrossRefGoogle Scholar
  15. 15.
    Seshadrinathan K, Bovik AC (2011) Automatic prediction of perceptual quality of multimedia signals - a survey. Multimed Tools Appl 51(1):163–186. doi: 10.1007/s11042-010-0625-9 CrossRefGoogle Scholar
  16. 16.
    Sheikh HR, Wang Z, Cormack L, Bovik AC (2013) LIVE Image Quality Assessment Database Release 2. http://live.ece.utexas.edu/research/Quality/. Accessed 26 Jan 2013
  17. 17.
    VQEG (2000) Final report from the video quality experts group on the validation of objective quality metrics for video quality assessment. http://www.its.bldrdoc.gov/vqeg/projects/frtv-phase-i/frtv-phase-i.aspx. Accessed 26 Jan 2013
  18. 18.
    Vu PV, Chandler DM (2012) A fast wavelet-based algorithm for global and local image sharpness estimation. IEEE Signal Process Lett 19(7):423–426. doi: 10.1109/LSP.2012.2199980 CrossRefGoogle Scholar
  19. 19.
    Vu CT, Phan TD, Chandler DM (2012) S3: A spectral and spatial measure of local perceived sharpness in natural images. IEEE Trans Image Process 21(3):934–945. doi: 10.1109/TIP.2011.2169974 MathSciNetCrossRefGoogle Scholar
  20. 20.
    Wang Z, Bovik AC (2009) Mean squared error: love it or leave it? a new look at signal fidelity measures. IEEE Signal Process Mag 26(1):98–117. doi: 10.1109/MSP.2008.930649 CrossRefGoogle Scholar
  21. 21.
    Wang Z, Bovik AC, Sheikh H, Simoncelli E (2004) Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 13(4):600–612CrossRefGoogle Scholar
  22. 22.
    Wu S, Lin W, Xie S, Lu Z, Ong EP, Yao S (2009) Blind blur assessment for vision-based applications. J Vis Commun Image Represent 20(4):231–241CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.Faculty of EngineeringUniversiti Malaysia SarawakKota SamarahanMalaysia
  2. 2.School of Electrical & Electronics EngineeringUniversiti Sains MalaysiaPenangMalaysia

Personalised recommendations