Skip to main content
SpringerLink
Log in

No-reference image quality assessment using fusion metric

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

Abstract

This paper presents a fusion featured metric for no-reference image quality assessment of natural images. Natural images exhibit strong statistical properties across the visual contents such as leading edge, high dimensional singularity, scale invariance, etc. The leading edge represents the strong presence of continuous points, whereas high singularity conveys about non-continuous points along the curves. Both edges and curves are equally important in perceiving the natural images. Distortions to the image affect the intensities of these points. The change in the intensities of these key points can be measured using SIFT. However, SIFT tends to ignore certain points such as the points in the low contrast region which can be identified by curvelet transform. Therefore, we propose a fusion of SIFT key points and the points identified by curvelet transform to model these changes. The proposed fused feature metric is computationally efficient and light on resources. The neruofuzzy classifier is employed to evaluate the proposed feature metric. Experimental results show a good correlation between subjective and objective scores for public datasets LIVE, TID2008, and TID2013.

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
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Benitz J, Castro J, Requena I (1997) Are artificial neural networks black boxes? IEEE Trans Neural Netw 8:1156–1164

    Article  Google Scholar 

  2. Bianco S, Celona L, Napoletano P, Schettini R (2017) On the use of deep learning for blind image quality assessment. SIViP 12:355–362. https://doi.org/10.1007/s11760-017-1166-8

    Article  Google Scholar 

  3. Chen MJ, Bovik AC (2009) No. reference image blur assessment using multiscale gradient. In: Proceeding of IEEE quality of multimedia experience, pp 70–74

  4. Djimeli A, Tchiotsop D, Tichinda R (2013) Analysis of interest points of curvelet coefficients contributions of microscopic images and improvement of edges. Signal and Image Processing: An International Journal (SIPIJ) 4

  5. Fan C, Zhang Y, Feng L, Jiang A (2018) No reference image quality assessment based on multi-expert convolution neural network. IEEE Access 6:8934–8943

    Article  Google Scholar 

  6. Fang Y, Ma K, Wang Z, Lin W, Fang Z, Zhai G (2015) No reference quality assessment of contrast distorted images based on natural scene statistics. IEEE Signal Processing Letters 22:838–842

    Google Scholar 

  7. Feng T, Deng D, Yan J, Zhang W, Shi W, Zou L (2016) Sparse representation of salient regions for no reference image quality assessment. Int J Adv Robot Syst. https://doi.org/10.1177/1729881416669486

    Article  Google Scholar 

  8. Gu K, Zhai G, Yang X, Zhang W (2015) Using free energy principle for blind image quality assessment. IEEE Transactions on Multimedia 17:50–63

    Article  Google Scholar 

  9. Hung Do Q, Chen J (2013) A neuro-fuzzy approach in the classification of students’ academic performance. Computational intelligence and neuroscience article ID 179097

  10. Jhang Y, Damon M, Chandler D (2013) An algorithm for no-reference image quality assessment based on log-derivative statistics of natural scenes. SPIE Proceedings: Image Quality and System Performance 8653:86530J-10

    Google Scholar 

  11. Kamble V, Bhurchandi KM (2015) No reference image quality assessment algorithm: a survey. Optik International Journal for Light & Electron Optics 126:1090–1097

    Article  Google Scholar 

  12. Keelan BW Handbook of image quality, characterization and prediction. Marcel Dekker Inc. ISBN 0-8247-0770-2

  13. Li L, Wu D, Wu J, Qian J, Chen B (2016) No reference image quality assessment with a gradient-induced dictionary. KSII Transactions on Internet and Information 10:288–306

    Google Scholar 

  14. Liu J, Yu X (2008) Research on SAR image matching technology based on SIFT. In: The international archives of the photogrammetry, remote sensing and spatial information sciences XXXVII part B1

  15. Liu L, Dong H, Huang H, Bovik A (2014) No reference image quality assessment in curvelet domain. Signal Process Image Commun 29:494–505

    Article  Google Scholar 

  16. Liu W, Li C, Chi Y, Sun X (2014) Image quality assessment based on SIFT and SSIM. In: Advances in image and graphics technologies, IGTA 2014, Communications in Computer and Information Science 437. Springer, Berlin Heidelberg

  17. Lu W, Zeng K, Tao D, Yuan Y, Gao X (2010) No-reference image quality assessment in contourlet. Neurocomputing 73:784–794

    Article  Google Scholar 

  18. Lv X, Qin M, Chen X, Wei G (2018) No reference image quality assessment based on statistics of convolution of feature maps. In: AIP conference proceeding 1995: 040034-1-040034-5. https://doi.org/10.1063/1.5033698

  19. Ma K, Liu W, Zhang K, Duanmu Z, Wang Z, Zuo W (2018) End-to-end blind image quality assessment using deep neural networks. IEEE Trans Image Process 27:1202_1213

    MathSciNet  MATH  Google Scholar 

  20. Mittal A, Moorthy A, Bovik AC (2011) Blind/referenceless image spatial quality evaluator. In: IEEE conference on signals, system and, computers, pp 723–727

  21. Mittal A, Moorthy AK, Bovik AC (2012) No reference image quality assessment in the spatial domain. IEEE Trans Image Process 21:4695–4707

    Article  MathSciNet  Google Scholar 

  22. Mittal A, Soundararajan R, Bovik A (2013) Making a completely blind image quality analyzer. IEEE Signal processing letters 20:209–213

    Article  Google Scholar 

  23. Moorthy AK, Bovik AC (2010) A two-step framework for constructing blind image quality indices. IEEE Signal Processing Letters 17:513–516

    Article  Google Scholar 

  24. Moorthy A, Bovik A (2011) Blind image quality assessment: from natural scene statistics to perceptual quality. IEEE Trans Image Process 20:3350–3364

    Article  MathSciNet  Google Scholar 

  25. Nizami I, Masid M, Khursid K (2018) Feature selection for no-reference image quality assessment using natural scene statistics. Turk J Electr Eng Comput Sci 26:2163–2177

    Article  Google Scholar 

  26. 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. Advances of Modern Radio electronics 10:30–45

    Google Scholar 

  27. Ponomarenko N, Battisti F, Egiazarian K, Astola J, Lukin V (2009) Metrics performance comparison for colour image database. In: Fourth international workshop on video processing and quality metrics for consumer electronics, vol 27, pp 1–6

  28. Ponomarenko N, Ieremeiev O, Lukin V, Egiazarian K, Jin L, Astola J, Vozel B, Chehdi K, Carli M, Battisti F, Kuo J (2013) Color image database TID2013: peculiarities and preliminary results. In: 4th European workshop on visual information processing EUVIP 106–111

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

    Article  Google Scholar 

  30. Qin M, Lv X, Chen X, Wang W (2017) Hybrid NSS features for no-reference image quality assessment. IET Image Process 11:443–449

    Article  Google Scholar 

  31. Qiu F (2008) Nero-fuzzy based analysis of hyperspectral imagery. Photogramm Eng Remote Sens 74:1235–1247

    Article  Google Scholar 

  32. Saad MA, Bovik AC, Charrier C (2012) Blind image quality assessment: a natural scene statistics approach in the DCT domain. IEEE Trans Image Process 21:3339–3351

    Article  MathSciNet  Google Scholar 

  33. Sheikh HR, Bovik AC, Cormack LK (2005) No-reference quality assessment using natural scene statistics: JPEG2000. IEEE Trans Image Process 14:1918–1927

    Article  Google Scholar 

  34. Sheikh HR, Wang Z, Cormack L, Bovik AC LIVE image quality assessment database release 2. http://live.ece.utexas.edu/research/quality.

  35. Telabi H, Milanfar (2018) NIMA: Neural image assessment. IEEE Trans Image Process 27:3998–4011

    Article  MathSciNet  Google Scholar 

  36. Wang Z, Bovik A (2002) Why is image quality assessment so difficult. IEEE Signal Processing Letters 4:3313–3316

    Google Scholar 

  37. Wang Z, Bovik A (2006) A lecture book on modern image quality assessment. Morgan and Claypool edition publisher

  38. Wang G, Wu Z, Yan H, Cui M (2016) No reference image quality assessment based on non-subsample shearlet transform and natural scene statistics. Optoelectron Lett 12

    Article  Google Scholar 

  39. Wei D, Lie Y (2016) No reference image quality assessment based on SIFT feature points. International Journal of Simulation: Systems, Science and Technology 17:17

    Google Scholar 

  40. Zhang D, Ding Y, Zheng N (2012) Nature scene statistics approach based on ICA for no reference image quality assessment. In: Proceedings of international workshop on information and electronics engineering (IWIEE), vol 29, pp 3589–3593

    Article  Google Scholar 

  41. Zhang Y, Moorthy A, Chandler D, Bovik A (2014) C-DIIVINE: no reference image quality assessment based on local magnitude and phase statistics of natural scenes. Signal Process Image Commun 29:725–747

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Information Technology, Vishwakarma Institute of Information Technology, Pune, Maharashtra, India

    Jayashri V. Bagade

  2. Department of Electronics and Communication Engineering, Thapar Institute of Engineering and Technology, Patiala, Punjab, India

    Kulbir Singh

  3. Department of Electronics and Telecommunication, Vishwakarma Institute of Information Technology, Pune, Maharashtra, India

    Y. H. Dandawate

Authors
  1. Jayashri V. Bagade
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kulbir Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Y. H. Dandawate
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jayashri V. Bagade.

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

Bagade, J.V., Singh, K. & Dandawate, Y.H. No-reference image quality assessment using fusion metric. Multimed Tools Appl 79, 2109–2125 (2020). https://doi.org/10.1007/s11042-019-08217-5

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-019-08217-5

Keywords

Search

Navigation