access icon free Efficient image structural similarity quality assessment method using image regularised feature

© The Institution of Engineering and Technology
Received 30/11/2019, Accepted 30/11/2020, Revised 25/05/2020, Published 14/12/2020

Image regularised features play a critical role in image processing domain, by integrating regularised feature and structural similarity, a new full-reference image assessment method (IRF_SSIM) is proposed in this study. As well known, the gradient operator always be used to capture the edge information of the image, while the total variational regularised features can be adopted to calculate the detailed change information of image contrast and texture, as well as noise removal and edge retention. Therefore, the IRF_SSIM method extends the gradient features into the image regularised features to measure the structural changes in the image. In addition, image quality is also affected by variations of luminance and contrast. For a more comprehensive image quality assessment, the IRF_SSIM method considers the changes in structure, luminance and contrast simultaneously. In other words, the total image quality is estimated by structural similarity calculated by integrating the effects of image structure, luminance and contrast changes. Comparing with the representative methods, the experimental results illustrate that the IRF_SSIM method is highly consistent with the subjective assessment results.

Inspec keywords: gradient methods; image texture; feature extraction; image resolution; image denoising

Other keywords: image processing domain; gradient features; comprehensive image quality assessment; image quality; full-reference image assessment method; luminance; image texture; total variational regularised features; gradient operator; total image quality; edge information; image contrast; edge retention; efficient image structural similarity quality assessment method; image structure; IRF_SSIM method; image regularised feature; noise removal

Subjects: Computer vision and image processing techniques; Image recognition; Optimisation techniques; Optimisation techniques

References

    1. 1)
      • 36. Bullimore, M.: ‘The psychophysical measurement of visual function’, Optometry Vis. Sci., 2002, 79, pp. 223–229.
    2. 2)
      • 8. Ding, Y., Wang, S., Zhang, D.: ‘Full-reference image quality assessment using statistical local correlation’, Electron. Lett., 2014, 50, (2), pp. 79–81.
    3. 3)
      • 41. Larson, E., Chandler, D.‘Categorical subjective image quality csiq database’, 2009.
    4. 4)
      • 27. Tang, L., Fang, Z., Xiang, C., et al: ‘A variational model for staircase reduction in image denoising’, Chin. J. Electron., 2017, 26, (2), pp. 358–366.
    5. 5)
      • 37. Uddin, A.F.M.S., Chung, T.C., Bae, S.: ‘Visual saliency based structural contrast quality index’, Electron. Lett., 2019, 55, (4), pp. 194–196.
    6. 6)
      • 18. Yao, J., Guizhong, L.: ‘Improved SSIM IQA of contrast distortion based on the contrast sensitivity characteristics of HVS’, IET Image Process., 2018, 12, (6), pp. 872–879.
    7. 7)
      • 48. Gaubatz, M., Hemami, S.: 2011‘Metrix mux visual quality assessment package’, foulard ece cornell edu/gaubatz/metrix_mux.
    8. 8)
      • 46. Ponomarenko, N., Jin, L., Ieremeiev, O., et al: ‘Image database tid2013: peculiarities, results and perspectives’, Signal Process., Image Commun., 2015, 30, pp. 57–77.
    9. 9)
      • 1. Wang, Z., Bovik, A.C.: ‘Modern image quality assessment: synthesis lectures on image, video and multimedia processing’, (Morgan & Claypool, USA, 2006), pp. 1–156.
    10. 10)
      • 25. Li, Q., Fang, Y., Lin, W., et al: ‘Gradient-weighted structural similarity for image quality assessments’. 2015 IEEE Int. Symp. on Circuits and Systems (ISCAS), Lisbon, Portugal, 2015, pp. 2165–2168.
    11. 11)
      • 38. Abdoli, M., Nasiri, F., Brault, P., et al: ‘Quality assessment tool for performance measurement of image contrast enhancement methods’, IET Image Process., 2019, 13, (5), pp. 833–842.
    12. 12)
      • 9. Liu, A., Lin, W., Narwaria, M.: ‘Image quality assessment based on gradient similarity’, IEEE Trans. Image Process., 2012, 21, (4), pp. 1500–1512.
    13. 13)
      • 24. Wang, Z., Bovik, A.C., Lu, L.: ‘Why is image quality assessment so difficult?’. 2002 IEEE Int. Conf. on Acoustics, Speech, and Signal Processing. vol. 4, Orlando, FL, USA, 2002, pp. IV-3313–IV-3316.
    14. 14)
      • 28. Huang, B., Pan, Z., Yang, H., et al: ‘Variational level set method for image segmentation with simplex constraint of landmarks’, Signal Process., Image Commun., 2020, 82, p. 115745.
    15. 15)
      • 32. Slepčv, D., Thorpe, M.: ‘Analysis of p-Laplacian regularization in semi-supervised learning’, SIAM J. Math. Anal., 2017, 51, pp. 2085–2120.
    16. 16)
      • 19. Chandler, D., Hemami, S.: ‘VSNR: a wavelet-based visual signal-to-noise ratio for natural images’, IEEE Trans. Image Process., 2007, 16, pp. 2284–2298.
    17. 17)
      • 13. Cui, Z., Gan, Z., Tang, G., et al: ‘Image signature based mean square error for image quality assessment’, Chin. J. Electron., 2015, 24, (4), pp. 755–760.
    18. 18)
      • 17. Bei, L., Meyer, G.W., Klassen, R.V.: ‘Comparison of two image quality models’. Proc. Spie the Int. Soci. Optical Eng., San Jose, CA, United States, 1998, 3299, (2), pp. 98–109.
    19. 19)
      • 22. Lin, W., Kuo, C.C.J.: ‘Perceptual visual quality metrics: a survey’, J. Vis. Commun. Image Represent., 2011, 22, pp. 297–312.
    20. 20)
      • 34. Xu, X., Yu, T., Xu, X., et al: ‘Variational total curvature model for multiplicative noise removal’, IET Comput. Vis., 2018, 12, (4), pp. 542–552.
    21. 21)
      • 29. Song, B.: ‘Topics in variational PDE image segmentation, inpainting and denoising’ (University of California, Los Angeles, 2003).
    22. 22)
      • 44. Chandler, D., Hemami, S.‘A57 database’, 2007.
    23. 23)
      • 7. Eskicioglu, A.M., Fisher, P.S.: ‘Image quality measures and their performance’, IEEE Trans. Commun., 1995, 43, (12), pp. 2959–2965.
    24. 24)
      • 12. Liu, D., Xu, Y., Quan, Y., et al: ‘Reduced reference image quality assessment using regularity of phase congruency’, Signal Process., Image Commun., 2014, 29, (8), pp. 844–855.
    25. 25)
      • 6. Girod, B.: ‘Psychovisual aspects of image processing: what's wrong with mean squared error?’. Proc. Seventh Workshop on Multidimensional Signal Processing, Lake Placid, NY, USA, 1991, pp. P.2–P.2.
    26. 26)
      • 40. Sheikh, H.R.: ‘Image and video quality assessment research at live’, http://live ece utexas edu/research/quality, 2003.
    27. 27)
      • 20. Wang, Z., Bovik, A.C.: ‘A universal image quality index’, IEEE Signal Process. Lett., 2002, 9, (3), pp. 81–84.
    28. 28)
      • 26. Liu, L., Zheng, Y., Wang, W.: ‘Image quality assessment scheme based on structural contrast index and gradient similarity’, 2017, pp. 325–331.
    29. 29)
      • 50. Cheng, G., Huang, J., Zhu, C., et al: ‘Perceptual image quality assessment using a geometric structural distortion model’. 2010 IEEE Int. Conf. on Image Processing, Hong Kong, People's Republic of China, 2010, pp. 325–328.
    30. 30)
      • 23. Cadik, M., Slavik, P.: ‘Evaluation of two principal approaches to objective image quality assessment’. Proc. Eighth Int. Conf. on Information Visualisation, 2004. IV 2004, 2004, pp. 513–518.
    31. 31)
      • 43. Engelke, U., Zepernick, H., Kusuma, M.: ‘Wireless imaging quality database’, Accessed date 2010.
    32. 32)
      • 10. Sheikh, H.R., Bovik, A.C.: ‘Image information and visual quality’, IEEE Trans. Image Process., 2006, 15, (2), pp. 430–444.
    33. 33)
      • 49. Sheikh, H., Bovik, A., Veciana, G.: ‘An information fidelity criterion for image quality assessment using natural scene statistics’, IEEE Trans. Image Process., 2006, 14, pp. 2117–2128.
    34. 34)
      • 42. Le-Callet, P., Autrusseau, F.: 2005‘Subjective quality assessment irccyn/ivc database’.
    35. 35)
      • 2. Sun, T., Ding, S., Chen, W., et al: ‘No-reference image quality assessment based on gradient histogram response’, Comput. Electr. Eng., 2016, 54, pp. 330–344.
    36. 36)
      • 47. Zhang, L., Zhang, L., Mou, X., et al: ‘A comprehensive evaluation of full reference image quality assessment algorithms’. 2012 19th IEEE Int. Conf. on Image Processing, Orlando, FL, USA, 2012, pp. 1477–1480.
    37. 37)
      • 35. Tan, L., Liu, W., Pan, Z.: ‘Color image restoration and inpainting via multi-channel total curvature’, Appl. Math. Model., 2018, 61, pp. 280–299.
    38. 38)
      • 11. Bhateja, V., Kalsi, A., Srivastava, A.: ‘Image similarity metric (isim): a reduced reference image quality assessment approach’, CSI Trans. ICT, 2015, 3, (1), pp. 1–11.
    39. 39)
      • 5. Cheon, M., Lee, J..: ‘On ambiguity of objective image quality assessment’, Electron. Lett., 2016, 52, (1), pp. 34–35.
    40. 40)
      • 15. Lai, Y.K., Kuo, C.C.J.: ‘A haar wavelet approach to compressed image quality measurement’, J. Vis. Commun. Image Represent., 2000, 11, (1), pp. 17–40.
    41. 41)
      • 21. Wang, Z., Simoncelli, E.P., Bovik, A.C.: ‘Multiscale structural similarity for image quality assessment’. The Thrity-Seventh Asilomar Conf. on Signals, Systems Computers, 2003, vol. 2, Pacific Grove, CA, USA, 2003, pp. 1398–1402.
    42. 42)
      • 39. Evans, B.L., Geisler, W.S.: 2001‘Rate scalable foveated image and video communications’.
    43. 43)
      • 33. Hinterberger, W., Scherzer, O.: ‘Variational methods on the space of functions of bounded hessian for convexification and denoising’, Computing, 2006, 76, pp. 109–133.
    44. 44)
      • 4. Wang, Z., Bovik, A.C., Sheikh, H.R., et al: ‘Image quality assessment: from error visibility to structural similarity’, IEEE Trans. Image Process., 2004, 13, (4), pp. 600–612.
    45. 45)
      • 3. Yuan, Y., Guo, Q., Lu, X.: ‘Image quality assessment: a sparse learning way’, Neurocomputing, 2015, 159, pp. 227–241.
    46. 46)
      • 16. Damera-Venkata, N., Kite, T., Geisler, W., et al: ‘Image quality assessment based on a degradation model’, IEEE Trans. Image Process., a publication of the IEEE Signal Processing Society, 2000, 9, pp. 636–650.
    47. 47)
      • 30. Li, Y., Osher, S.: ‘A new median formula with applications to PDE based denoising’, Commun. Math. Sci., 2009, 7, pp. 741–753.
    48. 48)
      • 14. Avcibas, I., Sankur, B., Sayood, K.: ‘Statistical evaluation of quality measures’, J. Electron. Imaging, 2002, 11, pp. 206–223.
    49. 49)
      • 31. Chan, T.F., Shen, J., Vese, L.: ‘Variational pde models in image processing’, Notices AMS, 2003, 50, (1), pp. 14–26.
    50. 50)
      • 45. Ponomarenko, N., Egiazarian, K.‘Tampere image database 2008 tid2008’, 2009.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-ipr.2019.1570
Loading

Related content

content/journals/10.1049/iet-ipr.2019.1570
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading