Skip to main content

Advertisement

SpringerLink
Log in

A new paradigm for image quality assessment based on human abstract layers of quality perception

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Image Quality Assessment (IQA) plays a central role in many visual processing algorithms and systems. Most of the existing IQA methods try to detect the image distortions’ type and then evaluate its severity. But each image distortion type has its own characteristics, which can harden up the process of quality evaluation. Here, we propose a novel framework for image quality assessment, in which the image is evaluated based on the human judgment process, comprises the three abstract perception layers, namely, illumination sensation, whole content detectability, and details perception. What distinguishes the proposed framework from the existing ones is its independence from the image distortion type. Indeed, we try to assess the amount of success in visual perception, instead of measuring the amount of image distortions. The proposed method has been extensively tested on four benchmark datasets and shows highly competitive performance to state-of-the-art IQA methods.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

Notes

  1. quality measure based on the Abstract 3 Layers (A3L) of human quality perception

  2. Mean Opinion Score

  3. Differential MOS

  4. Mean Squared Error

References

  1. A Vedaldi BF (2008) VLFeat: An open and portable library of computer vision algorithms. http://www.vlfeat.org/, accessed date: 20 june 2021

  2. Chandler DM (2013) Seven challenges in image quality assessment: past, present, and future research. ISRN Signal Process 2013:53. https://doi.org/10.1155/2013/905685

    Article  Google Scholar 

  3. Chandler DM, Hemami SS (2007) VSNR: A wavelet-based visual signal-to-noise ratio for natural images. IEEE Trans Image Process 16(9):2284–2298

  4. Cheng G, Cheng L, Li Y (2010) Wavelet-based directional structural distortion model for image quality assessment. Pattern Recogn Image Anal 20(3):286–292

    Article  Google Scholar 

  5. Fischler MA, Firschein O (2014) Readings in computer vision: Issues, problem, principles, and paradigms m. Elsevier Inc, Kaufmann Publishers

    Google Scholar 

  6. Frazor RA, Geisler WS (2006) Local luminance and contrast in natural images. Vis Res 46(10):1585–1598

    Article  Google Scholar 

  7. Group VQE (2003) Final report from the video quality experts group on the validation of objective models of video quality assessment

  8. Gu K, Zhai G, Lin W, Yang X, Zhang W (2015) No-reference image sharpness assessment in autoregressive parameter space. IEEE Trans Image Process 24(10):3218–3231

    Article  MathSciNet  Google Scholar 

  9. Gu K, Zhai G, Lin W, Liu M (2016) The analysis of image contrast: From quality assessment to automatic enhancement. IEEE Trans Cybern 46(1):284–97. https://doi.org/10.1109/TCYB.2015.2401732, http://www.ncbi.nlm.nih.gov/pubmed/25775503

  10. Kamble V, Bhurchandi K (2015) No-reference image quality assessment algorithms: a survey. Optik-Int J Light Electron Opt 126(11):1090–1097

    Article  Google Scholar 

  11. Kendall MG (1938) A new measure of rank correlation. Biometrika 30(1-2):81–93. https://doi.org/10.1093/biomet/30.1-2.81, http://biomet.oxfordjournals.org/content/30/1-2/81.short

  12. Khosravi MH, Hassanpour H (2016) Model-based full reference image blurriness assessment. Multimed Tools Appl:1–15. https://doi.org/10.1007/s11042-015-3149-5

  13. Khosravi MH, Hassanpour H (2017) A novel image structural similarity index considering image content detectability using maximally stable extremal region descriptor. Int J Eng-Trans B: Appl 30(2):1233–1242. https://doi.org/10.5829/idosi.ije.2017.30.02b.00

    Google Scholar 

  14. Khosravi MH, Hassanpour H (2019) Image quality assessment using a novel region smoothness measure. J Vis Commun Image Represent 60:217–228. https://doi.org/10.1016/j.jvcir.2018.11.019, http://www.sciencedirect.com/science/article/pii/S1047320318302906

  15. Khosravi MH, Hassanpour H (2020) Blind quality metric for contrast-distorted images based on eigendecomposition of color histograms. IEEE Trans Circ Syst Video Technol 30(1):48–58. https://doi.org/10.1109/tcsvt.2018.2890457

    Article  Google Scholar 

  16. Khosravi MH, Hassanpour H, Ahmadifard A (2017) A content recognizability measure for image quality assessment considering the high frequency attenuating distortions. Multimed Tools Appl 77(6):7357–7382. https://doi.org/10.1007/s11042-017-4636-7

    Article  Google Scholar 

  17. Larson EC, Chandler DM (2010) Most apparent distortion: full-reference image quality assessment and the role of strategy. J Electron Imaging 19(1):011,006–011,006–21

  18. Liu A, Lin W, Narwaria M (2012) Image quality assessment based on gradient similarity. IEEE Trans Image Process 21(4):1500–1512

    Article  MathSciNet  Google Scholar 

  19. Manap RA, Shao L (2015) Non-distortion-specific no-reference image quality assessment: A survey. Information Sciences

  20. Michelson AA (1927) Studies in optics. The University of Chicago Press, Chicago

  21. Moorthy AK, Bovik AC (2009) Visual importance pooling for image quality assessment. IEEE Jo Sel Top Signal Process 3(2):193–201

    Article  Google Scholar 

  22. Narwaria M, Lin W (2012) SVD-based quality metric for image and video using machine learning. IEEE Trans Syst Man Cybern Part B: Cybern 42(2):347–364

  23. 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 Modern Radioelectron 10(4):30–45

  24. Ponomarenko N, Jin L, Ieremeiev O, Lukin V, Egiazarian K, Astola J, Vozel B, Chehdi K, Carli M, Battisti F (2015) Image database TID2013: peculiarities, results and perspectives. Signal Process Image Commun 30:57–77

    Article  Google Scholar 

  25. Rehman A, Wang Z (2012) Reduced-reference image quality assessment by structural similarity estimation. IEEE Trans Image Process 21(8):3378–3389

    Article  MathSciNet  Google Scholar 

  26. Saad MA, Bovik AC, Charrier C (2012) Blind image quality assessment: a natural scene statistics approach in the DCT domain. IEEE Trans Image Process 21(8):3339–3352

    Article  MathSciNet  Google Scholar 

  27. Sheikh HR, Bovik AC (2006) Image information and visual quality. IEEE Trans Image Process 15(2):430–444

    Article  Google Scholar 

  28. Sheikh HR, Bovik AC, De Veciana G (2005a) An information fidelity criterion for image quality assessment using natural scene statistics. IEEE Trans Image Process 14(12):2117–2128

    Article  Google Scholar 

  29. Sheikh HR, Wang Z, Cormack L, Bovik AC (2005b) LIVE image quality assessment database release 2

  30. Sheikh HR, Sabir MF, Bovik AC (2006) A statistical evaluation of recent full reference image quality assessment algorithms. IEEE Trans Image Process 15(11):3440–51, http://www.ncbi.nlm.nih.gov/pubmed/17076403

  31. Shnayderman A, Gusev A, Eskicioglu AM (2006) An SVD-based grayscale image quality measure for local and global assessment. IEEE Trans Image Process 15(2):422–429

    Article  Google Scholar 

  32. Soundararajan R, Bovik AC (2012) RRED indices: Reduced reference entropic differencing for image quality assessment. IEEE Trans Image Process 21(2):517–526

  33. Wang T, Zhang L, Jia H, Li B, Shu H (2016) Multiscale contrast similarity deviation: an effective and efficient index for perceptual image quality assessment. Signal Processing: Image Communication

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

    Article  Google Scholar 

  35. Wang Z, Li Q (2011) Information content weighting for perceptual image quality assessment. IEEE Trans Image Process 20(5):1185–1198

    Article  MathSciNet  Google Scholar 

  36. Wang Z, Simoncelli EP, Bovik AC (2003) Multiscale structural similarity for image quality assessment. In: Proceedings of the 37th IEEE asilomar conference on signals, systems and computers, vol 2, pp 1398–1402

  37. Wang Z, Bovik AC, Sheikh HR, Simoncelli EP (2004) Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 13(4):600–612

    Article  Google Scholar 

  38. Wu J, Lin W, Shi G, Liu A (2013) Perceptual quality metric with internal generative mechanism. IEEE Trans Image Process 22(1):43–54

    Article  MathSciNet  Google Scholar 

  39. Xue W, Zhang L, Mou X, Bovik A (2013) Gradient magnitude similarity deviation: a highly efficient perceptual image quality index. IEEE Trans Image Process 23(2):684–695. https://doi.org/10.1109/TIP.2013.2293423

    Article  MathSciNet  Google Scholar 

  40. Yang J, Lin Y, Ou B, Zhao X (2016) Image decomposition-based structural similarity index for image quality assessment. EURASIP J Image Video Process 2016(1):31. https://doi.org/10.1186/s13640-016-0134-5

    Article  Google Scholar 

  41. Ye P, Kumar J, Kang L, Doermann D (2012) Unsupervised feature learning framework for no-reference image quality assessment. In: 2012 IEEE Conference on Computer vision and pattern recognition (CVPR). IEEE, pp 1098–1105

  42. Zhang L, Zhang D, Mou X (2010) Rfsim: A feature based image quality assessment metric using riesz transforms. In: 2010 17th IEEE International Conference on Image Processing (ICIP), pp 321–324

  43. Zhang L, Zhang D, Mou X (2011) FSIM: a feature similarity index for image quality assessment. IEEE Trans Image Process 20(8):2378–2386

Download references

Author information

Authors and Affiliations

  1. Faculty of Electrcial and Computer Engineering, University of Birjand, Birjand, Iran

    Mohammad Hossein Khosravi

  2. Faculty of Computer Engineering and Information Technology, Shahrood University of Technology, Shahrood, Iran

    Hamid Hassanpour

Authors
  1. Mohammad Hossein Khosravi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hamid Hassanpour
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mohammad Hossein Khosravi.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Khosravi, M.H., Hassanpour, H. A new paradigm for image quality assessment based on human abstract layers of quality perception. Multimed Tools Appl 81, 23193–23215 (2022). https://doi.org/10.1007/s11042-022-12478-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-022-12478-y

Keywords

Search

Navigation