Skip to main content
SpringerLink
Log in

Recent Advances in Image Quality Assessment

  • Chapter
  • First Online:
Visual Signal Quality Assessment

Abstract

Over the last few decades, IQA has been an increasingly popular research topic in the fields of image processing and computer vision. In this chapter we will survey some recent advances in image quality assessment (IQA). This chapter is organized into three sections: subjective IQA, objective IQA, and new directions in IQA. More specifically, the first section will review widely used IQA databases, with emphasis on those recently proposed ones. The second section will review quality metrics in the familiar categorization of full-reference (FR), reduced-reference (RR), and no-reference (NR) ones. The third section introduces some emerging and interesting research directions including comparative IQA, multiply-distorted IQA, contrast-changed IQA, which we believe will be important topics for the future study of perceptual quality assessment.

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 89.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 119.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover 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. A. C. Bovik, “Automatic prediction of perceptual image and video quality,” Proceedings of the IEEE, vol. 101, no. 9, pp. 2008–2024, September 2013,

    MathSciNet  Google Scholar 

  2. Z. Wang and A. C. Bovik, “Mean squared error: Love it or leave it?-A new look at signal fidelity measures,” IEEE Signal Process. Mag., vol. 26, no. 1, pp. 98–117, January 2009.

    Article  Google Scholar 

  3. G. Zhai, J. Cai, W. Lin, X. Yang, and W. Zhang, “Three dimensional scalable video adaptation via user-end perceptual quality assessment,” IEEE Trans. Broadcasting, vol. 54, no. 3, pp. 719–727, September 2008.

    Article  Google Scholar 

  4. G. Zhai, J. Cai, W. Lin, X. Yang, W. Zhang, and M. Etoh, “Cross-dimensional perceptual quality assessment for low bitrate videos,” IEEE Trans. Multimedia, vol. 10, no. 7, pp. 1316–1324, November 2008.

    Article  Google Scholar 

  5. H. R. Sheikh, Z. Wang, L. Cormack, and A. C. Bovik, “LIVE image quality assessment Database Release 2,” [Online]. Available: http://live.ece.utexas.edu/research/quality

  6. N. Ponomarenko, V. Lukin, A. Zelensky, K. Egiazarian, M. Carli, and F. Battisti, “TID2008-A database for evaluation of full-reference visual quality assessment metrics,” Advances of Modern Radioelectronics, vol. 10, pp. 30–45, 2009.

    Google Scholar 

  7. E. C. Larson and D. M. Chandler, “Categorical image quality (CSIQ) database,” [Online], Available: http://vision.okstate.edu/csiq

  8. N. Ponomarenko, O. Ieremeiev, V. Lukin, K. Egiazarian, L. Jin, J. Astola, B. Vozel, K. Chehdi, M. Carli, F. Battisti, and C.-C. Jay Kuo, “Color image database TID2013: Peculiarities and preliminary results,” 4th European Workshop on Visual Information Processing EUVIP2013, pp.106–111, June 2013.

    Google Scholar 

  9. D. Jayaraman, A. Mittal, A. K. Moorthy, and A. C. Bovik, “Objective quality assessment of multiply distorted images,” Proc. IEEE Asilomar Conference on Signals, Systems and Computers, pp. 1693–1697, November 2012.

    Google Scholar 

  10. K. Gu, G. Zhai, X. Yang, W. Zhang, and M. Liu, “Subjective and objective quality assessment for images with contrast change,” Proc. IEEE Int. Conf. Image Process., pp. 383–387, September 2013.

    Google Scholar 

  11. M. Liu, G. Zhai, S. Tan, Z. Zhang, K. Gu, and X. Yang, “HDR2014 - A high dynamice range image quality database,” Proc. IEEE Int. Conf. Multimedia and Expo Workshops, 2014.

    Google Scholar 

  12. Kodak Lossless True Color Image Suite: http://r0k.us/graphics/kodak/

  13. H. R. Sheikh, M. F. Sabir, and A. C. Bovik, “A statistical evaluation of recent full reference image quality assessment algorithms,” IEEE Trans. Image Process., vol. 15, no. 11, pp. 3440–3451, November 2006.

    Article  Google Scholar 

  14. Z. Wang and A. C. Bovik, “A universal image quality index,” IEEE Signal Processing Letters, vol. 9, pp. 81–84, March 2002.

    Article  Google Scholar 

  15. Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: From error visibility to structural similarity,” IEEE Trans. Image Process., vol. 13, no. 4, pp. 600–612, April 2004.

    Article  Google Scholar 

  16. Z. Wang, E. P. Simoncelli, and A. C. Bovik, “Multi-scale structural similarity for image quality assessment,” IEEE Asilomar Conference Signals, Systems and Computers, pp. 1398–1402, November 2003.

    Google Scholar 

  17. M. Liu, G. Zhai, K. Gu, Q. Xu, X. Yang, X. Sun, W. Chen, and Y. Zuo, “A new image quality metric based on MIx-Scale transform,” Proc. IEEE Workshop on Signal Processing Systems, pp. 266–271, October 2013.

    Google Scholar 

  18. E. Peli, “Contrast in complex images,” Journal of Optical Society of America, vol. 7, pp. 2032–2040, October 1990.

    Article  Google Scholar 

  19. K. Gu, G. Zhai, X. Yang, and W. Zhang, “Self-adaptive scale transform for IQA metric,” Proc. IEEE Int. Symp. Circuits and Syst., pp. 2365–2368, May 2013.

    Google Scholar 

  20. K. Gu, G. Zhai, M. Liu, Q. Xu, X. Yang, and W. Zhang, “Adaptive high-frequency clipping for improved image quality assessment,” Proc. IEEE Visual Communications and Image Processing, pp. 1–5, November 2013.

    Google Scholar 

  21. H. Liu and I. Heynderickx, “Visual attention in objective image quality assessment: Based on eye-tracking data,” IEEE Trans. Circuits Syst. Video Technol., vol. 21, no. 7, pp. 971–982, April 2011.

    Article  Google Scholar 

  22. X. Min, G. Zhai, Z. Gao, and K. Gu, “Visual attention data for image quality assessment databases,” Proc. IEEE Int. Symp. Circuits and Syst., 2014.

    Google Scholar 

  23. K. Gu, G. Zhai, X. Yang, L. Chen, and W. Zhang, “Nonlinear additive model based saliency map weighting strategy for image quality assessment”, IEEE International Workshop on Multimedia Signal Processing, pp. 313–318, September 2012.

    Google Scholar 

  24. L. Itti, C. Koch, and E. Niebur, “A model of saliency-based visual attention for rapid scene analysis,” IEEE Transaction on Pattern Analysis and Machine Intelligence, vol. 20, pp. 1254–1259, November 1998.

    Article  Google Scholar 

  25. Z. Wang and Q. Li, “Information content weighting for perceptual image quality assessment,” IEEE Trans. Image Process., vol. 20, no. 5, pp. 1185–1198, 2011.

    Article  MathSciNet  Google Scholar 

  26. E. P. Simoncelli and B. A. Olshausen, “Natural image statistics and neural representation,” Annual Review of Neuroscience, vol. 24, no. 1, pp. 1193–1216, 2001.

    Article  Google Scholar 

  27. K. Gu, G. Zhai, X. Yang, W. Zhang, and M. Liu, “Structural similarity weighting for image quality assessment,” Proc. IEEE Int. Conf. Multimedia and Expo Workshops, pp. 1–6, July 2013.

    Google Scholar 

  28. L. Zhang, L. Zhang, X. Mou, and D. Zhang, “FSIM: A feature similarity index for image quality assessment,” IEEE Trans. Image Process., vol. 20, no. 8, pp. 2378–2386, August 2011.

    Article  MathSciNet  Google Scholar 

  29. A. Liu, W. Lin, and M. Narwaria, “Image quality assessment based on gradient similarity,” IEEE Trans. Image Process., vol. 21, no. 4, pp. 1500–1512, April 2012.

    Article  MathSciNet  Google Scholar 

  30. W. Xue, L. Zhang, X. Mou, and A. C. Bovik, “Gradient magnitude similarity deviation: A highly efficient perceptual image quality index,” IEEE Trans. Image Process., vol. 23, no. 2, pp. 684–695, February 2014.

    Article  MathSciNet  Google Scholar 

  31. K. Gu, G. Zhai, X. Yang, J. Zhou, X. Gu, and W. Zhang, “An efficient color image quality metric with local-tuned-global model,” Proc. IEEE Int. Conf. Image Process., 2014.

    Google Scholar 

  32. B. J\(\ddot{a}\)hne, H. Haubecker, and P. Geibler, Handbook of Computer Vision and Applications. New York: Academic, 1999.

    Google Scholar 

  33. C. Yang and S. H. Kwok, “Efficient gamut clipping for color image processing using LHS and YIQ,” Optical Engineering, vol. 42, no. 3, pp. 701–711, March 2003.

    Article  Google Scholar 

  34. H. R. Sheikh, and A. C. Bovik, “Image information and visual quality,” IEEE Trans. Image Process., vol. 15, no. 2, pp. 430–444, February 2006.

    Article  Google Scholar 

  35. G. Zhai, W. Zhang, X. Yang, S. Yao, and Y. Xu, “GES: a new image quality assessment metric based on energy features in Gabor transform domain,” Proc. IEEE Int. Symposium on Circuits and Systems, pp. 1715–1718, 2006.

    Google Scholar 

  36. G. Zhai, W. Zhang, Y. Xu, and W. Lin, “LGPS: Phase based image quality assessment metric,” IEEE Workshop on Signal Processing Systems, pp. 605–609, 2007.

    Google Scholar 

  37. E. C. Larson and D. M. Chandler, “Most apparent distortion: Full-reference image quality assessment and the role of strategy,” Journal of Electronic Imaging, vol. 19, no. 1, March 2010.

    Google Scholar 

  38. J. Wu, W. Lin, G. Shi, and A. Liu, “Perceptual quality metric with internal generative mechanism,” IEEE Trans. Image Process., vol. 22, no. 1, pp. 43–54, January 2013.

    Article  MathSciNet  Google Scholar 

  39. F. Zhang, W. Jiang, F. Autrusseau, and W. Lin, “Exploring V1 by modeling the perceptual quality of images,” Journal of Vision, vol. 14, no. 1, pp. 1–14, 2014.

    Article  Google Scholar 

  40. K. Friston, J. Kilner, and L. Harrison, “A free energy principle for the brain,” Journal of Physiology Paris, vol. 100, pp. 70–87, 2006.

    Article  Google Scholar 

  41. K. Friston, “The free-energy principle: A unified brain theory?” Nature Reviews Neuroscience, vol. 11, pp. 127–138, 2010.

    Article  Google Scholar 

  42. D. C. Knill and A. Pouget, “The Bayesian brain: The role of uncertainty in neural coding and computation,” Trends Neurosci., vol. 27, no. 12, pp. 712–719, 2004.

    Article  Google Scholar 

  43. G. Zhai, X. Wu, X. Yang, W. Lin, and W. Zhang, “A psychovisual quality metric in free-energy principle,” IEEE Trans. Image Process., vol. 21, no. 1, pp. 41–52, January 2012.

    Article  MathSciNet  Google Scholar 

  44. R. Soundararajan and A. C. Bovik, “RRED indices: Reduced-reference entropic differencing for image quality assessment,” IEEE Trans. Image Process., vol. 21, no. 2, pp. 517–526, February 2012.

    Article  MathSciNet  Google Scholar 

  45. M. Narwaria, W. Lin, I. V. McLoughlin, S. Emmanuel, and L. T. Chia, “Fourier transform-based scalable image quality measure,” IEEE Trans. Image Process., vol. 21, no. 8, pp. 3364–3377, August 2012.

    Article  MathSciNet  Google Scholar 

  46. A. Rehman and Z. Wang, “Reduced-reference image quality assessment by structural similarity estimation,” IEEE Trans. Image Process., vol. 21, no. 8, pp. 3378–3389, August 2012.

    Article  MathSciNet  Google Scholar 

  47. K. Gu, G. Zhai, X. Yang, and W. Zhang, “A new reduced-reference image quality assessment using structural degradation model,” Proc. IEEE Int. Symp. Circuits and Syst., pp. 1095–1098, May 2013.

    Google Scholar 

  48. Z. Wang, H. R. Sheikh, and A. C. Bovik, “No-reference perceptual quality assessment of JPEG compressed images,” Proc. IEEE Int. Conf. Image Process., pp. 477–480, September 2002.

    Google Scholar 

  49. D. Zoran and Y. Weiss, “Scale invariance and noise in natural images,” Proc. IEEE Int. Conf. Comput. Vis., pp. 2209–2216, September 2009.

    Google Scholar 

  50. G. Zhai and X. Wu, “Noise estimation using statistics of natural images,” Proc. IEEE Int. Conf. Image Process., pp. 1857–1860, September 2011.

    Google Scholar 

  51. X. Liu, M. Tanaka, and M. Okutomi, “Single-image noise level estimation for blind denoising,” IEEE Trans. Image Process., vol. 22, no. 12, pp. 5226–5237, December 2013.

    Article  Google Scholar 

  52. P. Marziliano, F. Dufaux, S. Winkler, and T. Ebrahimi, “A no-reference perceptual blur metric,” Proc. IEEE Int. Conf. Image Process., vol. 3, pp. 57–60, 2002.

    Google Scholar 

  53. R. Ferzli and L. Karam, “A no-reference objective image sharpness metric based on the notion of just noticeable blur (JNB),” IEEE Trans. Image Process., vol. 18, no. 4, pp. 717–728, April 2009.

    Article  MathSciNet  Google Scholar 

  54. N. D. Narvekar and L. J. Karam, “A no-reference image blur metric based on the cumulative probability of blur detection (CPBD),” IEEE Trans. Image Process., vol. 20, no. 9, pp. 2678–2683, September 2011.

    Article  MathSciNet  Google Scholar 

  55. C. Vu, T. Phan, and D. Chandler, “S3: A spectral and spatial measure of local perceived sharpness in natural images,” IEEE Trans. Image Process., vol. 21, no. 3, pp. 934–945, March 2012.

    Article  MathSciNet  Google Scholar 

  56. P. Vu and D. Chandler, “A fast wavelet-based algorithm for global and local image sharpness estimation,” IEEE Signal Process. Lett., vol. 19, no. 7, pp. 423–426, July 2012.

    Article  Google Scholar 

  57. C. Feichtenhofer, H. Fassold, and P. Schallauer, “A perceptual image sharpness metric based on local edge gradient analysis,” IEEE Signal Process. Lett., vol. 20, no. 4, pp. 379–382, April 2013.

    Article  Google Scholar 

  58. Z. Wang and E. Simoncelli, “Local phase coherence and the perception of blur,” in Advances in Neural Information Processing Systems, vol. 16, pp. 1–8. Cambridge, MA, USA: MIT Press, May 2004.

    Google Scholar 

  59. R. Hassen, Z. Wang, and M. Salama, “Image sharpness assessment based on local phase coherence,” IEEE Trans. Image Process., vol. 22, no. 7, pp. 2798–2810, July 2013.

    Article  Google Scholar 

  60. A. K. Moorthy and A. C. Bovik, “Blind image quality assessment: From scene statistics to perceptual quality,” IEEE Trans. Image Process., pp. 3350–3364, vol. 20, no. 12, December 2011.

    Google Scholar 

  61. M. A. Saad, A. C. Bovik, and C. Charrier, “Blind image quality assessment: A natural scene statistics approach in the DCT domain,” IEEE Trans. Image Process., pp. 3339–3352, vol. 21, no. 8, August 2012.

    Google Scholar 

  62. A. Mittal, A. K. Moorthy, and A. C. Bovik, “No-reference image quality assessment in the spatial domain,” IEEE Trans. Image Process., pp. 4695–4708, vol. 21, no. 12, December 2012.

    Google Scholar 

  63. K. Gu, G. Zhai, X. Yang, W. Zhang, and L. Liang, “No-reference image quality assessment metric by combining free energy theory and structural degradation model,” Proc. IEEE Int. Conf. Multimedia and Expo, pp. 1–6, July 2013.

    Google Scholar 

  64. D. Martin, C. Fowlkes, D. Tal, and J. Malik, “A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics,” Proc. IEEE Int. Conf. Comput. Vis., pp. 416–423, 2001.

    Google Scholar 

  65. A. Mittal, R. Soundararajan, and A. C. Bovik, “Making a ‘completely blind’ image quality analyzer,” IEEE Signal Process. Letters, pp. 209–212, vol. 22, no. 3, March 2013.

    Google Scholar 

  66. W. Xue, L. Zhang, and X. Mou, “Learning without human scores for blind image quality assessment,” Proc. IEEE Int. Conf. Comput. Vis. and Pattern Recognition, pp. 995–1002, June 2013.

    Google Scholar 

  67. G. Zhai and A. Kaup, “Comparative image quality assessment using free energy minimization,” Proc. IEEE Int. Conf. Acoustics, Speech, and Signal Processing, pp. 1884–1888, 2013.

    Google Scholar 

  68. K. Gu, G. Zhai, M. Liu, X. Yang, W. Zhang, X. Sun, W. Chen, and Y. Zuo, “FISBLIM: A five-step blind metric for quality assessment of multiply distorted images,” Proc. IEEE Workshop on Signal Processing Systems, pp. 241–246, October 2013.

    Google Scholar 

  69. K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, “Image denoising by sparse 3-D transform-domain collaborative filtering,” IEEE Trans. Image Process., vol. 16, no. 8, pp. 2080–2095, August 2007.

    Article  MathSciNet  Google Scholar 

  70. X. Wu, “A linear programming approach for optimal contrast-tone mapping,” IEEE Trans. Image Process., vol. 20, no. 5, pp. 1262–1272, May. 2011.

    Google Scholar 

  71. I. Motoyoshi, S. Nishida, L. Sharan, and E. H. Adelson, “Image statistics and the perception of surface qualities,” Nature, vol. 447, pp. 206–209, May 2007.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Shanghai Jiao Tong University, Shanghai, China

    Guangtao Zhai

Authors
  1. Guangtao Zhai
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Guangtao Zhai .

Editor information

Editors and Affiliations

  1. School of Information and Electronics, Beijing Institute of Technology, Beijing, China

    Chenwei Deng

  2. Huawei Noah’s Ark Lab, Quarry Bay, Hong Kong SAR

    Lin Ma

  3. School of Computer Engineering, Nanyang Technological University, Singapore

    Weisi Lin

  4. Department of Electronic Engineering, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong SAR

    King Ngi Ngan

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Zhai, G. (2015). Recent Advances in Image Quality Assessment. In: Deng, C., Ma, L., Lin, W., Ngan, K. (eds) Visual Signal Quality Assessment. Springer, Cham. https://doi.org/10.1007/978-3-319-10368-6_3

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-10368-6_3

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-10367-9

  • Online ISBN: 978-3-319-10368-6

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation