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No reference image quality assessment with shape adaptive discrete wavelet features using neuro-wavelet model

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Abstract

No reference method for image quality assessment using shape adaptive wavelet features by applying neuro-wavelet model is proposed in this paper. Images usually consist of visual objects. Degradation of an image ultimately causes distortions to the objects present in the image. Distortions can change the shape of these objects. Quality assessment of an image cannot be said to be complete without assessing the quality of individual objects present in the image. Therefore, deviation in shape has to be quantified along with the quality assessment of an image. Shape Adaptive Discrete Wavelet Transform offers a solution to shape identification problem. The variations in magnitude of feature values are found not proportional to the amount of degradation due to the presence of other artifacts. Wavelet decomposition is applied to capture the small variations observed in extracted features. Separate back propagation neural network models are trained for quality assessment of all kind of images ranging from pristine to bad. Results show improvement in accuracy independent of image databases. It has been observed that the predicted score correlates well with the mean opinion score with 90% accuracy for LIVE dataset, 93% and 95% for TID2008 and TID2013 respectively.

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References

  1. Avcibas I, Sankur B, Sayood K (2002) Static evaluation of image quality measures. J Electron Imaging 11:206–223

    Article  Google Scholar 

  2. Bagade J, Dandawate Y, Singh. K (2011) No reference image quality assessment using block-based features and artificial neural network. 4th international conference on communication in computer and information science springer-Verlag Berlin Heidelberg, 270:128–138

  3. Bagade J, Singh K, Dandawate Y (2014) No reference image quality assessment using block-based and frequency domain statistical features: a machine learning approach. Int J Commun Netw Distrib Syst 12:95–112

    Google Scholar 

  4. Bagade J, Singh K, Dandawate Y (2020) No-reference image quality assessment using fusion metric. Multimed Tools Appl 79:2109–2125

    Article  Google Scholar 

  5. Bianco S, Celona L, Napoletano P, Schettini R (2017) On the use of deep learning for blind image quality assessment. Signal Image Video Process 12:355–362

    Article  Google Scholar 

  6. Bosse S, Maniry D, Wiegand T, Samek W (2016) A deep neural network for image quality assessment. IEEE international conference on image processing (ICIP) 3773–3777

  7. Cagnazzo M, Poggi G, Verdoliva L, Zinicola A (2004) Region-oriented compression of multispectral images by shape-adaptive wavelet transform and SPIHT. Proceedings of IEEE international conference on image processing (ICIP ‘04) 4: 2459–2462

  8. Campos RG, Salles EOT (2019) Robust statistics and no-reference image quality assessment in Curvelet domain. XIV workshop of Computer Vision and Pattern Recognition

  9. Chandler D (2013) Seven challenges in image quality assessment: past, present and future research. ISRN Signal processing Article ID 905685, 53 pages

  10. Chen M, Bovik A (2009) No. reference image blur assessment using multiscale gradient. In: IEEE quality of multimedia experience San Diego Canada 70-74

  11. Chen X, Zhang Q, Lin M (2019) No-reference color image quality assessment: from entropy to perceptual quality. EURASIP J Image Video Process 2019:7

    Article  Google Scholar 

  12. Eskicioglu A, Fisher P (1995) Image quality measures and their performances. IEEE Trans Commun 43:2959–2965

    Article  Google Scholar 

  13. 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 Process Lett 22:838–842

    Google Scholar 

  14. Gadre V, Abhyankar A (2017) Multiresolution & multirate signal processing: introduction, principles & applications. McGraw Hills Education

    Google Scholar 

  15. Gastaldo P, Zunino R, Heynderickx I, Vicario E (2005) Objective quality assessment of displayed images by using neural networks. Signal Process Image Commun 20:643–661

    Article  Google Scholar 

  16. Kang L, Ye P, Li Y, Doermann D. (2014) Convolutional neural networks for no-reference image quality assessment. IEEE Conference on Computer Vision and Pattern Recognition 1733–1740

  17. Lee S, Park SJ (2012) A new image quality assessment method to detect and measure strength of blocking artifacts. J Signal Process Image Commun 27:31–38

    Article  Google Scholar 

  18. Li S, Li W (2000) Shape-adaptive discrete wavelet transforms for arbitrarily shaped visual object coding. IEEE Trans Circuits Syst Video Technol 10:725–743

    Article  Google Scholar 

  19. Li L, Wu D, Wu J, Qian J, Chen B (2016) No reference image quality assessment with a gradient-induced dictionary. KSII Trans Internet Inf 10:288–306

    Google Scholar 

  20. 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 

  21. Liu X, Weijer J, Bagdanov A. (2017) RankIQA: learning from rankings for no reference image quality assessment. IEEE international conference on computer vision 1040-1049

  22. 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 

  23. Mallat S (2008) A wavelet tour of signal processing: the sparse way. Academic Press, Elsevier, Burlington

    MATH  Google Scholar 

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

    Article  MathSciNet  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  27. 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 Radio Electron 10:30–45

    Google Scholar 

  28. Ponomarenko N, Battisti F, Egiazarian, K, Astola J, Lukin V. (2009) Metrics performance comparison for colour image database. Fourth International Workshop on Video Processing and Quality Metrics for Consumer Electronics 27: 1–6

  29. 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. 4th European workshop on visual information processing EUVIP 106-111

  30. 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 

  31. Saad M, Bovik A, Charier C (2010) A DCT statistics-based blind image quality index. IEEE Single Process Lett 17:583–586

    Article  Google Scholar 

  32. Saad M, Bovik A, 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. Shahid M, Rossholm A, Lovstrom B, Zepernick H (2014) No reference image and video quality assessment: a classification and review of recent approaches. EURASIP J Image Video Process 2014:40

    Article  Google Scholar 

  34. Sheikh HR, Sabir MF, Bovik A (2006) A statistical evaluation of recent full reference image quality assessment algorithms. IEEE Trans Image Process 15:3440–3345

    Article  Google Scholar 

  35. Sheikh H, Bovik A, Cormack L (2005) No-reference quality assessment using natural scene statistics: JPEG2000. IEEE Trans Image Process 14:1918–1927

    Article  Google Scholar 

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

  37. Suresh S, Venkatesh Babu R, Kim H (2009) No-reference image quality assessment using modified extreme learning machine classifier. Appl Soft Comput 9:541–552

    Article  Google Scholar 

  38. Suthaharan S (2002) No-reference visually significant blocking artifact metric for natural scene images. Signal Process 89:1647–1652

    Article  Google Scholar 

  39. Varga D (2021) No-reference image quality assessment with global statistical features. J Imaging 7:29

    Article  Google Scholar 

  40. Wang Z, Bovik A (2002) A universal image quality index. IEEE Signal Process Lett 9:81–84

    Article  Google Scholar 

  41. Wang Z, Bovik A (2006) Modern image quality assessment. Morgan & Claypool, Modern Image Quality Assessment, New York

    Book  Google Scholar 

  42. Wang Z, Bovik A, Sheikh H, Simoncelli E (2004) Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 13:600–612

    Article  Google Scholar 

  43. 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:–156

  44. Zhai G, Zhang W, Yang X, Lin W, Xu Y (2008) No-reference noticeable blockiness estimation in images. Signal Process 23:417–432

    Google Scholar 

  45. Zhang X, Feng X, Wang W, Xue W (2013) Edge strength similarity for image quality assessment. IEEE Signal Process Lett 20:319–322

    Article  Google Scholar 

  46. 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 

  47. Zhou L, Zhang Z (2014) No reference image quality assessment based on noise, blurring and blocking effect. Optik 125:5677–5680

    Article  Google Scholar 

Download references

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The authors have no relevant financial or non-financial interests to disclose.

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Authors and Affiliations

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

    Jayashri V. Bagade

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

    Kulbir Singh

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

    Yogesh H. Dandawate

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  1. Jayashri V. Bagade
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  2. Kulbir Singh
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  3. Yogesh H. Dandawate
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Correspondence to Jayashri V. Bagade.

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Bagade, J.V., Singh, K. & Dandawate, Y.H. No reference image quality assessment with shape adaptive discrete wavelet features using neuro-wavelet model. Multimed Tools Appl 81, 31145–31160 (2022). https://doi.org/10.1007/s11042-022-12983-0

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