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Natural scene statistics model independent no-reference image quality assessment using patch based discrete cosine transform

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Abstract

Most of no-reference image quality assessment (NR-IQA) techniques reported in literature have utilized transform coefficients, which are modeled using curve fitting to extract features based on natural scene statistics (NSS). The performance of NR-IQA techniques that utilize curve-fitting suffers from degradation in performance because the distribution of curve fitted NSS features deviate from the statistical distribution of a distorted image. Although deep convolutional neural networks (DCNNs) have been used for NR-IQA that are NSS model-independent but their performance is dependent upon the size of training data. The available datasets for NR-IQA are small, therefore data augmentation is used that affects the performance of DCNN based NR-IQA techniques and is also computationally expensive. This work proposes a new patch-based NR-IQA technique, which utilizes features extracted from discrete cosine transform coefficients. The proposed technique is curve fitting independent and helps in avoiding errors in the statistical distribution of NSS features. It relies on global statistics to estimate image quality based on local patches, which allow us to decompose the statistics of images. The proposed technique divides the image into patches and extracts nine handcrafted features i.e., entropy, mean, variance, skewness, kurtosis, mobility, band power, energy, complexity, and peak to peak value. The extracted features are used with a support vector regression model to predict the image quality score. The experimental results have shown that the proposed technique is database and image content-independent. It shows better performance over a majority of distortion types and on images taken in real-time.

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References

  1. Bai X, Zhang T, Wang C, El-Latif AAA, Niu X (2013) A fully automatic player detection method based on one-class svm. IEICE Trans Inf Syst 96 (2):387–391

    Google Scholar 

  2. Benrhouma O, Hermassi H, El-Latif AAA, Belghith S (2016) Chaotic watermark for blind forgery detection in images. Multimed Tools Appl 75 (14):8695–8718

    Google Scholar 

  3. Bosse S, Maniry D, Muller K-R, Wiegand T, Samek W (2017) Deep neural networks for no-reference and full-reference image quality assessment. IEEE Trans Image Process 27(1):206–219

    MathSciNet  MATH  Google Scholar 

  4. Chandler DM (2013) Seven challenges in image quality assessment: past, present, and future research. ISRN Signal Process 2013:1–54

    Google Scholar 

  5. Chang C-C, Lin C-J (2011) Libsvm: a library for support vector machines. ACM Trans Intell Syst Technol (TIST) 2(3):27

    Google Scholar 

  6. Fan Y, Zhu Y, Zhai G, Wang J, Liu J (2018) A data-driven no-reference image quality assessment via deep convolutional neural networks. In: Pacific rim conference on multimedia. Springer, pp 361–371

  7. Gao F, Wang Y, Li P, Tan M, Yu J, Zhu Y (2017) Deepsim: deep similarity for image quality assessment. Neurocomputing 257:104–114

    Google Scholar 

  8. Ghadiyaram D, Bovik AC (2016) Massive online crowdsourced study of subjective and objective picture quality. IEEE Trans Image Process 25(1):372–387

    MathSciNet  MATH  Google Scholar 

  9. Gu K, Zhai G, Yang X, Zhang W (2015) Using free energy principle for blind image quality assessment. IEEE Trans Multimed 17(1):50–63

    Google Scholar 

  10. Gu K, Wang S, Yang H, Lin W, Zhai G, Yang X, Zhang W (2016) Saliency-guided quality assessment of screen content images. IEEE Trans Multimed 18(6):1098–1110

    Google Scholar 

  11. He L, Zhong Y, Lu W, Gao X (2019) A visual residual perception optimized network for blind image quality assessment. IEEE Access 7:176087–176098

    Google Scholar 

  12. Heydari M, Cheraaqee P, Mansouri A, Mahmoudi-Aznaveh A (2019) A low complexity wavelet-based blind image quality evaluator. Signal Process: Image Commun 74:280–288

    Google Scholar 

  13. Huang Y, Chen X, Ding X (2016) A harmonic means pooling strategy for structural similarity index measurement in image quality assessment. Multimed Tools Appl 75(5):2769–2780

    Google Scholar 

  14. Huynh-Thu Q, Ghanbari M (2008) Scope of validity of psnr in image/video quality assessment. Electron Lett 44(13):800–801

    Google Scholar 

  15. Jiang Q, Shao F, Lin W, Gu K, Jiang G, Sun H (2018) Optimizing multistage discriminative dictionaries for blind image quality assessment. IEEE Trans Multimed 20(8):2035–2048

    Google Scholar 

  16. Jing H, He X, Han Q, El-Latif AAA, Niu X (2014) Saliency detection based on integrated features. Neurocomputing 129:114–121

    Google Scholar 

  17. Kalatehjari E, Yaghmaee F (2018) A new reduced-reference image quality assessment based on the svd signal projection. Multimed Tools Appl 77 (19):25053–25076

    Google Scholar 

  18. Khan M, Nizami IF, Majid M (2019) No-reference image quality assessment using gradient magnitude and wiener filtered wavelet features. Multimed Tools Appl 78(11):14485–14509

    Google Scholar 

  19. Khosravi MH, Hassanpour H, Ahmadifard A (2018) A content recognizability measure for image quality assessment considering the high frequency attenuating distortions. Multimed Tools Appl 77(6):7357–7382

    Google Scholar 

  20. Kim J, Lee S (2016) Fully deep blind image quality predictor. IEEE J Sel Top Signal Process 11(1):206–220

    Google Scholar 

  21. Larson EC, Chandler DM (2010) Most apparent distortion: full-reference image quality assessment and the role of strategy. J Electron Imaging 19 (1):011006–011006

    Google Scholar 

  22. Li Y, Po L-M, Xu X, Feng L, Yuan F, Cheung C-H, Cheung K-W (2015) No-reference image quality assessment with shearlet transform and deep neural networks. Neurocomputing 154:94–109

    Google Scholar 

  23. Li Y, Po L-M, Feng L, Yuan F (2016) No-reference image quality assessment with deep convolutional neural networks. In: 2016 IEEE international conference on digital signal processing (DSP). IEEE, pp 685–689

  24. Liao X, Li K, Yin J (2017) Separable data hiding in encrypted image based on compressive sensing and discrete fourier transform. Multimed Tools Appl 76(20):20739–20753

    Google Scholar 

  25. Liao X, Qin Z, Ding L (2017) Data embedding in digital images using critical functions. Signal Process: Image Commun 58:146–156

    Google Scholar 

  26. Lin W, Kuo C-CJ (2011) Perceptual visual quality metrics: a survey. J Vis Commun Image Represent 22(4):297–312

    Google Scholar 

  27. Lin H, Hosu V, Saupe D (2019) Kadid-10k: a large-scale artificially distorted iqa database. In: 2019 eleventh international conference on quality of multimedia experience (QoMEX). IEEE, pp 1–3

  28. Liu A, Wang J, Liu J, Su Y (2019) Comprehensive image quality assessment via predicting the distribution of opinion score. Multimed Tools Appl 78 (17):24205–24222

    Google Scholar 

  29. Liu Y, Zhai G, Gu K, Liu X, Zhao D, Gao W (2018) Reduced-reference image quality assessment in free-energy principle and sparse representation. IEEE Trans Multimed 20(2):379–391

    Google Scholar 

  30. Lu W, Xu T, Ren Y, He L (2016) Statistical modeling in the shearlet domain for blind image quality assessment. Multimed Tools Appl 75 (22):14417–14431

    Google Scholar 

  31. Lv Y, Jiang G, Yu M, Xu H, Shao F, Liu S (2015) Difference of gaussian statistical features based blind image quality assessment: a deep learning approach. In: 2015 IEEE international conference on image processing (ICIP). IEEE, pp 2344–2348

  32. Ma K, Liu W, Zhang K, Duanmu Z, Wang Z, Zuo W (2017) End-to-end blind image quality assessment using deep neural networks. IEEE Trans Image Process 27(3):1202–1213

    MathSciNet  MATH  Google Scholar 

  33. Min X, Gu K, Zhai G, Liu J, Yang X, Chen CW (2018) Blind quality assessment based on pseudo-reference image. IEEE Trans Multimed 20 (8):2049–2062

    Google Scholar 

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

    MathSciNet  MATH  Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

  37. Moorthy AK, Bovik AC (2011) Blind image quality assessment: from natural scene statistics to perceptual quality. IEEE Trans Image Process 20 (12):3350–3364

    MathSciNet  MATH  Google Scholar 

  38. Moorthy AK, Bovik AC (2011) Visual quality assessment algorithms: what does the future hold? Multimed Tools Appl 51(2):675–696

    Google Scholar 

  39. Nill N (1985) A visual model weighted cosine transform for image compression and quality assessment. IEEE Trans Commun 33(6):551–557

    Google Scholar 

  40. Nizami IF, Majid M, Khurshid K (2018) New feature selection algorithms for no-reference image quality assessment. Appl Intell 48(10):3482–3501

    Google Scholar 

  41. Nizami IF, Majid M, Afzal H, Khurshid K (2018) Impact of feature selection algorithms on blind image quality assessment. Arab J Sci Eng 43(8):4057–4070

    Google Scholar 

  42. Nizami IF, Majid M, Manzoor W, Khurshid K, Jeon B (2019) Distortion-specific feature selection algorithm for universal blind image quality assessment. EURASIP J Image Video Process 2019(1):19

    Google Scholar 

  43. Nizami IF, Majid M, ur Rehman M, Anwar SM, Nasim A, Khurshid K (2020) No-reference image quality assessment using bag-of-features with feature selection. Multimed Tools Appl 79:7811–7836

    Google Scholar 

  44. Ponomarenko N, Jin L, Ieremeiev O, Lukin V, Egiazarian K, Astola J, Vozel B, Chehdi K, Carli M, Battisti F et al (2015) Image database tid2013: peculiarities, results and perspectives. Signal Process: Image Commun 30:57–77

    Google Scholar 

  45. Rezaie F, Helfroush MS, Danyali H (2018) No-reference image quality assessment using local binary pattern in the wavelet domain. Multimed Tools Appl 77(2):2529–2541

    Google Scholar 

  46. Reisenhofer R, Bosse S, Kutyniok G, Wiegand T (2018) A haar wavelet-based perceptual similarity index for image quality assessment. Signal Process: Image Commun 61:33–43

    Google Scholar 

  47. Saad MA, Bovik AC, Charrier C (2010) A dct statistics-based blind image quality index. IEEE Signal Process Lett 17(6):583–586

    Google Scholar 

  48. 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

    MathSciNet  MATH  Google Scholar 

  49. Sadiq A, Nizami IF, Anwar SM, Majid M (2020) Blind image quality assessment using natural scene statistics of stationary wavelet transform. Optik 49:205

    Google Scholar 

  50. Sheikh HR, Sabir MF, Bovik AC (2006) A statistical evaluation of recent full reference image quality assessment algorithms. IEEE Trans Image Process 15(11):3440–3451

    Google Scholar 

  51. Siahaan E, Hanjalic A, Redi JA (2018) Semantic-aware blind image quality assessment. Signal Process: Image Commun 60:237–252

    Google Scholar 

  52. Smola AJ, Schölkopf B (2004) A tutorial on support vector regression. Stat Comput 14(3):199–222

    MathSciNet  Google Scholar 

  53. Temel D, AlRegib G (2019) Perceptual image quality assessment through spectral analysis of error representations. Signal Process: Image Commun 70:37–46

    Google Scholar 

  54. 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

    Google Scholar 

  55. Wu Q, Li H, Wang Z, Meng F, Luo B, Li W, Ngan KN (2017) Blind image quality assessment based on rank-order regularized regression. IEEE Trans Multimed 19(11):2490–2504

    Google Scholar 

  56. Wu J, Zeng J, Dong W, Shi G, Lin W (2019) Blind image quality assessment with hierarchy: degradation from local structure to deep semantics. J Vis Commun Image Represent 58:353–362

    Google Scholar 

  57. Xue W, Mou X, Zhang L, Bovik AC, Feng X (2014) Blind image quality assessment using joint statistics of gradient magnitude and laplacian features. IEEE Trans Image Process 23(11):4850–4862

    MathSciNet  MATH  Google Scholar 

  58. Yan X, Wang S, Li L, El-Latif AAA, Wei Z, Niu X (2013) A new assessment measure of shadow image quality based on error diffusion techniques. J Inf Hiding Multimed Signal Process (JIHMSP) 4(2):118–126

    Google Scholar 

  59. Yan X, Wang S, El-Latif AAA, Sang J, Niu X (2014) A novel perceptual secret sharing scheme. In: Transactions on data hiding and multimedia security IX. Springer, pp 68–90

  60. Yan X, Wang S, El-Latif AAA, Niu X (2015) Random grids-based visual secret sharing with improved visual quality via error diffusion. Multimed Tools Appl 74(21):9279–9296

    Google Scholar 

  61. Yang X, Sun Q, Wang T (2018) Image quality assessment improvement via local gray-scale fluctuation measurement. Multimed Tools Appl 77 (18):24185–24202

    Google Scholar 

  62. 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. IEEE, pp 1098–1105

  63. Zhang M, Muramatsu C, Zhou X, Hara T, Fujita H (2015) Blind image quality assessment using the joint statistics of generalized local binary pattern. IEEE Signal Process Lett 22(2):207–210

    Google Scholar 

  64. Zhang Y, Wu J, Xie X, Li L, Shi G (2016) Blind image quality assessment with improved natural scene statistics model. Digit Signal Process 57:56–65

    MathSciNet  Google Scholar 

  65. Zhang W, Zou W, Yang F (2019) Linking visual saliency deviation to image quality degradation: a saliency deviation-based image quality index. Signal Process: Image Commun 75:168–177

    Google Scholar 

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Authors and Affiliations

  1. Department of Electrical Engineering, Bahria University, Islamabad, 44000, Pakistan

    Imran Fareed Nizami

  2. Department of Avionics Engineering, Air University, Islamabad, 44000, Pakistan

    Mobeen ur Rehman

  3. Department of Computer Engineering, University of Engineering and Technology Taxila, Taxila, 47050, Pakistan

    Muhammad Majid

  4. Department of Software Engineering, University of Engineering and Technology Taxila, Taxila, 47050, Pakistan

    Syed Muhammad Anwar

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  1. Imran Fareed Nizami
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Correspondence to Imran Fareed Nizami.

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Nizami, I.F., Rehman, M.u., Majid, M. et al. Natural scene statistics model independent no-reference image quality assessment using patch based discrete cosine transform. Multimed Tools Appl 79, 26285–26304 (2020). https://doi.org/10.1007/s11042-020-09229-2

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