Skip to main content
SpringerLink
Log in

PIQI: perceptual image quality index based on ensemble of Gaussian process regression

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

Abstract

Digital images contain a lot of redundancies, therefore, compression techniques are applied to reduce the image size without loss of reasonable image quality. Same become more prominent in the case of videos which contains image sequences and higher compression ratios are achieved in low throughput networks. Assessment of quality of images in such scenarios has become of particular interest. Subjective evaluation in most of the scenarios is infeasible so objective evaluation is preferred. Among the three objective quality measures, full-reference and reduced-reference methods require an original image in some form to calculate the image quality which is unfeasible in scenarios such as broadcasting, acquisition or enhancement. Therefore, a no-reference Perceptual Image Quality Index (PIQI) is proposed in this paper to assess the quality of digital images which calculates luminance and gradient statistics along with mean subtracted contrast normalized products in multiple scales and color spaces. These extracted features are provided to a stacked ensemble of Gaussian Process Regression (GPR) to perform the perceptual quality evaluation. The performance of the PIQI is checked on six benchmark databases and compared with twelve state-of-the-art methods and competitive results are achieved. The comparison is made based on RMSE, Pearson and Spearman’s correlation coefficients between ground truth and predicted quality scores. The scores of 0.0552, 0.9802 and 0.9776 are achieved respectively for these metrics on CSIQ database. Two cross-dataset evaluation experiments are performed to check the generalization of PIQI.

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
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  1. N. Ahmed and H. M. S. Asif (2009) Ensembling Convolutional Neural Networks for Perceptual Image Quality Assessment, 2019 13th International Conference on Mathematics, Actuarial Science, Computer Science and Statistics (MACS), Karachi, Pakistan, pp. 1-5, https://doi.org/10.1109/MACS48846.2019.9024822

  2. Ahmed N., Asif H.M.S., Khalid H. (2020) Image Quality Assessment Using a Combination of Hand-Crafted and Deep Features. In: Bajwa I., Sibalija T., Jawawi D. (eds) Intelligent Technologies and Applications. INTAP 2019. Communications in Computer and Information Science, vol 1198. Springer, Singapore. https://doi.org/10.1007/978-981-15-5232-8_51

  3. D. Ghadiyaram, J. Pan, A. C. Bovik, A. K. Moorthy, P. Panda and K. Yang (2018) In-Capture Mobile Video Distortions: A Study of Subjective Behavior and Objective Algorithms, in IEEE Transactions on Circuits and Systems for Video Technology, vol. 28, no. 9, pp. 2061-2077, https://doi.org/10.1109/TCSVT.2017.2707479

  4. Bianco S, Celona L, Napoletano P, Schettini R (2018) On the use of deep learning for blind image quality assessment. SIViP 12(2):355–362

    Article  Google Scholar 

  5. Bosse S, Maniry D, Muller KR, Wiegand T, Samek W (2018) Deep neural networks for no-reference and full-reference image quality assessment. IEEE Trans Image Process 27(1):206–219

    Article  MathSciNet  Google Scholar 

  6. Cai H et al (2019) Towards a blind image quality evaluator using multi-scale second-order statistics. Signal Process Image Commun 71:88–99

    Article  Google Scholar 

  7. Chang H-W, Yang H, Gan Y, Wang MH (2013) Sparse feature fidelity for perceptual image quality assessment. IEEE Trans Image Process 22(10):4007–4018

    Article  MathSciNet  Google Scholar 

  8. Charrier C, Lézoray O, Lebrun G (2012) Machine learning to design full-reference image quality assessment algorithm. Signal Process Image Commun 27(3):209–219

    Article  Google Scholar 

  9. Chen M-J, Bovik AC (2011) No-reference image blur assessment using multiscale gradient. EURASIP J Image Video Process 2011(1):3

    Article  Google Scholar 

  10. Dixit MM (2020) Image quality assessment of modified adaptable VQ used in DCT based image compression schemes implemented on DSP and FPGA platforms. Multimed Tools Appl 79(1):163–182

    Article  Google Scholar 

  11. Fu B, Zhao X, Li Y, Wang X, Ren Y (2019) A convolutional neural networks denoising approach for salt and pepper noise. Multimed Tools Appl 78(21):30707–30721

    Article  Google Scholar 

  12. Ghadiyaram D et al (2017) In-capture mobile video distortions: a study of subjective behavior and objective algorithms. IEEE Trans Circ Syst Video Technol

  13. Heydari M et al (2019) A low complexity wavelet-based blind image quality evaluator. Signal Process Image Commun 74:280–288

    Article  Google Scholar 

  14. N. Lasmar, Y. Stitou and Y. Berthoumieu (2009) Multiscale skewed heavy tailed model for texture analysis, 2009 16th IEEE International Conference on Image Processing (ICIP), Cairo, pp. 2281-2284. https://doi.org/10.1109/ICIP.2009.5414404

  15. Li Q, Lin W, Gu K, Zhang Y, Fang Y (2019) Blind image quality assessment based on joint log-contrast statistics. Neurocomputing 331:189–198

    Article  Google Scholar 

  16. 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 

  17. Liu L et al (2016) Blind image quality assessment by relative gradient statistics and adaboosting neural network. Signal Process Image Commun 40:1–15

    Article  Google Scholar 

  18. Ma K, Liu W, Liu T, Wang Z, Tao D (2017) dipIQ: blind image quality assessment by learning-to-rank discriminable image pairs. IEEE Trans Image Process 26(8):3951–3964

    Article  MathSciNet  Google Scholar 

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

    Article  MathSciNet  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  23. Nadeem M, Hussain A, Munir A (2019) Fuzzy logic based computational model for speckle noise removal in ultrasound images. Multimed Tools Appl 78(13):18531–18548

    Article  Google Scholar 

  24. 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

    Article  Google Scholar 

  25. F. Ou, Y. Wang and G. Zhu (2019) A Novel Blind Image Quality Assessment Method Based on Refined Natural Scene Statistics, 2019 IEEE International Conference on Image Processing (ICIP), Taipei, Taiwan, pp. 1004-1008.

  26. Ponomarenko N et al (2013) Color image database TID2013: Peculiarities and preliminary results. In: Visual Information Processing (EUVIP), 2013 4th European Workshop on. IEEE

  27. Reisenhofer R et al (2018) A Haar wavelet-based perceptual similarity index for image quality assessment. Signal Process Image Commun 61:33–43

    Article  Google Scholar 

  28. Ruderman DL, Bialek W (1994) Statistics of natural images: scaling in the woods. Phys Rev Lett 73(6):814–817

    Article  Google Scholar 

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

    Article  Google Scholar 

  30. 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 

  31. Sadiq A, Nizami IF, Anwar SM, Majid M (2020) Blind image quality assessment using natural scene statistics of stationary wavelet transform. Optik 205:164189

    Article  Google Scholar 

  32. Sharifi K, Leon-Garcia A (1995) Estimation of shape parameter for generalized Gaussian distributions in subband decompositions of video. IEEE Trans Circ Syst Video Technol 5(1):52–56

    Article  Google Scholar 

  33. H. R. Sheikh and A. C. Bovik (2006) Image information and visual quality, in IEEE Transactions on Image Processing, vol. 15, no. 2, pp. 430-444. https://doi.org/10.1109/TIP.2005.859378

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

    Article  Google Scholar 

  35. Shen J, Li Q, Erlebacher G (2011) Hybrid no-reference natural image quality assessment of Noisy, blurry, JPEG2000, and JPEG images. IEEE Trans Image Process 20(8):2089–2098

    Article  MathSciNet  Google Scholar 

  36. Shen L, Hang N, Hou C (2020) Feature-segmentation strategy based convolutional neural network for no-reference image quality assessment. Multimed Tools Appl:1–14

  37. H. Tang, N. Joshi and A. Kapoor (2011) Learning a blind measure of perceptual image quality, CVPR 2011, Providence, RI, pp. 305-312. https://doi.org/10.1109/CVPR.2011.5995446

  38. Varga, D (2020) Composition-preserving deep approach to full-reference image quality assessment. SIViP 14, 1265–1272. https://doi.org/10.1007/s11760-020-01664-w

  39. Z. Wan, K. Gu and D. Zhao (2020) Reduced Reference Stereoscopic Image Quality Assessment Using Sparse Representation and Natural Scene Statistics, in IEEE Transactions on Multimedia, vol. 22, no. 8, pp. 2024-2037. https://doi.org/10.1109/TMM.2019.2950533

  40. Wang Z, Bovik AC (2006) Modern image quality assessment. Synth Lect Image Video Multimedia Process 2(1):1–156

    Article  Google Scholar 

  41. Wang Z, Simoncelli EP, Bovik AC (2003) Multiscale structural similarity for image quality assessment, The Thrity-Seventh Asilomar Conference on Signals, Systems & Computers, 2003, Pacific Grove, CA, USA, pp. 1398-1402 Vol.2, https://doi.org/10.1109/ACSSC.2003.1292216

  42. 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 

  43. Wang Z, Sun Y, Li G, Ooi BT (2010) Magnitude and frequency control of grid-connected doubly fed induction generator based on synchronised model for wind power generation. IET Renew Power Gener 4(3):232–241

  44. J. Xu, Q. Li, P. Ye, H. Du and Y. Liu (2015) Local feature aggregation for blind image quality assessment, 2015 Visual Communications and Image Processing (VCIP), Singapore, 2015, pp. 1-4, https://doi.org/10.1109/VCIP.2015.7457832

  45. Xu J, Ye P, Li Q, du H, Liu Y, Doermann D (2016) Blind image quality assessment based on high order statistics aggregation. IEEE Trans Image Process 25(9):4444–4457

    Article  MathSciNet  Google Scholar 

  46. 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

    Article  MathSciNet  Google Scholar 

  47. P. Ye, J. Kumar, L. Kang and D. Doermann (2012) Unsupervised feature learning framework for no-reference image quality assessment, 2012 IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, pp. 1098-1105. https://doi.org/10.1109/CVPR.2012.6247789

  48. Zhang S, He F (2019) DRCDN: learning deep residual convolutional dehazing networks. Vis Comput:1–12

  49. Zhang L et al (2011) FSIM: a feature similarity index for image quality assessment. IEEE Trans Image Process 20(8):2378–2386

    Article  MathSciNet  Google Scholar 

  50. Zhang L, Zhang L, Bovik AC (2015) A feature-enriched completely blind image quality evaluator. IEEE Trans Image Process 24(8):2579–2591

    Article  MathSciNet  Google Scholar 

  51. Zhang J, He F, Chen Y (2020) A new haze removal approach for sky/river alike scenes based on external and internal clues. Multimed Tools Appl 79(3):2085–2107

    Article  Google Scholar 

  52. Zhou Z-H, Wu J, Tang W (2002) Ensembling neural networks: many could be better than all. Artif Intell 137(1–2):239–263

    Article  MathSciNet  Google Scholar 

  53. Zhuang P, Ding X (2020) Underwater image enhancement using an edge-preserving filtering Retinex algorithm. Multimed Tools Appl:1–21

Download references

Author information

Authors and Affiliations

  1. Department of Computer Engineering, University of Engineering and Technology, Lahore, 54890, Pakistan

    Nisar Ahmed

  2. Department of Computer Science, University of Engineering and Technology, Lahore, 54890, Pakistan

    Hafiz Muhammad Shahzad Asif

  3. Space and Upper Atmosphere Research Commission, Karachi, 8402, Sindh, Pakistan

    Hassan Khalid

Authors
  1. Nisar Ahmed
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hafiz Muhammad Shahzad Asif
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hassan Khalid
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Nisar Ahmed.

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

Ahmed, N., Asif, H.M.S. & Khalid, H. PIQI: perceptual image quality index based on ensemble of Gaussian process regression. Multimed Tools Appl 80, 15677–15700 (2021). https://doi.org/10.1007/s11042-020-10286-w

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-020-10286-w

Keywords

Search

Navigation