Skip to main content
SpringerLink
Log in

GIP: Generic Image Prior for No Reference Image Quality Assessment

  • Conference paper
  • First Online:
Advances in Multimedia Information Processing - PCM 2016 (PCM 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9917))

Included in the following conference series:

Abstract

No reference image quality assessment (NR-IQA) has attracted great attention due to the increasing demand in developing perceptually friendly applications. The crucial challenge of this task is how to accurately measure the naturalness of an image. In this paper, we propose a novel parametric image representation which is derived from the generic image prior (GIP). More specifically, we utilize the classic fields of experts model to capture the prior distribution of an image with respect to a random field, which is learned from a great deal of natural images. Then, the parameters in modeling this prior distribution are used as the quality-relevant image feature, which is represented by a simple two-dimension vector. Experimental results show that the proposed method achieves competitive quality prediction accuracy in comparison with the state-of-the-art NR-IQA algorithms at the expense of much less memory usage and computational complexity.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

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

Institutional subscriptions

References

  1. Chen, D., Cao, X., Wen, F., Sun, J.: Blessing of dimensionality: high-dimensional feature and its efficient compression for face verification. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 3025–3032 (2013)

    Google Scholar 

  2. Clarke, B., Fokoue, E., Zhang, H.: Principles and Theory for Data Mining and Machine Learning. Springer Series in Statistics. Springer, New York (2009)

    Book  MATH  Google Scholar 

  3. Dennis Jr., J.E., Moré, J.J.: Quasi-newton methods, motivation and theory. SIAM Rev. 19(1), 46–89 (1977)

    Article  MathSciNet  MATH  Google Scholar 

  4. Fang, Y., Ma, K., Wang, Z., Lin, W., Fang, Z., Zhai, G.: No-reference quality assessment of contrast-distorted images based on natural scene statistics. IEEE Sig. Process. Lett. 22(7), 838–842 (2015)

    Google Scholar 

  5. Fang, Y., Zeng, K., Wang, Z., Lin, W., Fang, Z., Lin, C.W.: Objective quality assessment for image retargeting based on structural similarity. IEEE J. Emerg. Sel. Top. Circ. Syst. 4(1), 95–105 (2014)

    Article  Google Scholar 

  6. Han, J., Pei, J., Kamber, M.: Data Mining, Southeast Asia Edition. The Morgan Kaufmann Series in Data Management Systems. Elsevier, Amsterdam (2006)

    Google Scholar 

  7. Hsu, C.W., Chang, C.C., Lin, C.J., et al.: A practical guide to support vector classification, Technical report (2003)

    Google Scholar 

  8. Martin, D., Fowlkes, C., Tal, D., Malik, J.: A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In: IEEE International Conference on Computer Vision, vol. 2, pp. 416–423 (2001)

    Google Scholar 

  9. Mittal, A., Moorthy, A.K., Bovik, A.C.: No-reference image quality assessment in the spatial domain. IEEE Trans. Image Process. 21(12), 4695–4708 (2012)

    Article  MathSciNet  Google Scholar 

  10. Mittal, A., Soundararajan, R., Bovik, A.C.: Making a “completely blind” image quality analyzer. IEEE Signal Process. Lett. 20(3), 209–212 (2013)

    Article  Google Scholar 

  11. Moorthy, A.K., Bovik, A.C.: Blind image quality assessment: From natural scene statistics to perceptual quality. IEEE Trans. Image Process. 20(12), 3350–3364 (2011)

    Article  MathSciNet  Google Scholar 

  12. Moorthy, A., Bovik, A.: A two-step framework for constructing blind image quality indices. IEEE Sig. Process. Lett. 17(5), 513–516 (2010)

    Article  Google Scholar 

  13. Redner, R.A., Walker, H.F.: Mixture densities, maximum likelihood and the em algorithm. SIAM Rev. 26(2), 195–239 (1984)

    Article  MathSciNet  MATH  Google Scholar 

  14. Roth, S., Black, M.: Fields of experts: a framework for learning image priors. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 860–867 (2005)

    Google Scholar 

  15. Saad, M., Bovik, A.C., Charrier, C.: Blind image quality assessment: a natural scene statistics approach in the dct domain. IEEE Trans. Image Process. 21(8), 3339–3352 (2012)

    Article  MathSciNet  Google Scholar 

  16. Schölkopf, B., Smola, A.J., Williamson, R.C., Bartlett, P.L.: New support vector algorithms. Neural Comput. 12(5), 1207–1245 (2000)

    Article  Google Scholar 

  17. Sheikh, H.R., Wang, Z., Cormack, L., Bovik, A.C.: LIVE Image Quality Assessment Database Release 2. http://live.ece.utexas.edu/research/quality

  18. Wang, Z.: Applications of objective image quality assessment methods [applications corner]. IEEE Sig. Process. Mag. 28(6), 137–142 (2011)

    Article  Google Scholar 

  19. Welling, M., Osindero, S., Hinton, G.E.: Learning sparse topographic representations with products of student-t distributions. In: Advances in Neural Information Processing Systems, pp. 1359–1366 (2002)

    Google Scholar 

  20. Welling, M., Osindero, S., Hinton, G.E.: Learning sparse topographic representations with products of student-t distributions. In: Becker, S., Thrun, S., Obermayer, K. (eds.) Advances in Neural Information Processing Systems, pp. 1383–1390 (2003)

    Google Scholar 

  21. Wu, Q., Li, H., Meng, F., Ngan, K.N., Luo, B., Huang, C., Zeng, B.: Blind image quality assessment based on multichannel feature fusion and label transfer. IEEE Trans. Circ. Syst. Video Technol. 26(3), 425–440 (2016)

    Article  Google Scholar 

  22. Wu, Q., Wang, Z., Li, H.: A highly efficient method for blind image quality assessment. In: IEEE International Conference on Image Processing, pp. 339–343 (2015)

    Google Scholar 

  23. Wu, Q., Li, H., Meng, F., Ngan, K.N., Zhu, S.: No reference image quality assessment metric via multi-domain structural information and piecewise regression. J. Vis. Commun. Image Representation 32, 205–216 (2015)

    Article  Google Scholar 

  24. Ye, P., Doermann, D.: No-reference image quality assessment using visual codebooks. IEEE Trans. Image Process. 21(7), 3129–3138 (2012)

    Article  MathSciNet  Google Scholar 

  25. Ye, P., Kumar, J., Kang, L., Doermann, D.: Unsupervised feature learning framework for no-reference image quality assessment. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1098–1105 (2012)

    Google Scholar 

Download references

Acknowledgement

This work was supported in part by National Natural Science Foundation of China (No. 61525102, No. 61271289), and by The program for Science and Technology Innovative Research Team for Young Scholars in Sichuan Province, China (No. 2014TD0006).

Author information

Authors and Affiliations

  1. University of Electronic Science and Technology of China, Chengdu, 611731, China

    Qingbo Wu, Hongliang Li & King N. Ngan

  2. Chinese University of Hong Kong, ShaTin, Hong Kong

    King N. Ngan

Authors
  1. Qingbo Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hongliang Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. King N. Ngan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Qingbo Wu .

Editor information

Editors and Affiliations

  1. Zhengzhou University, Zhengzhou, China

    Enqing Chen

  2. Jiaotong University, Xi’an, China

    Yihong Gong

  3. Zhengzhou University, Zhengzhou, China

    Yun Tie

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing AG

About this paper

Cite this paper

Wu, Q., Li, H., Ngan, K.N. (2016). GIP: Generic Image Prior for No Reference Image Quality Assessment. In: Chen, E., Gong, Y., Tie, Y. (eds) Advances in Multimedia Information Processing - PCM 2016. PCM 2016. Lecture Notes in Computer Science(), vol 9917. Springer, Cham. https://doi.org/10.1007/978-3-319-48896-7_59

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-48896-7_59

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-48895-0

  • Online ISBN: 978-3-319-48896-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation