Currently, the structural similarity index metric (SSIM) is recognised generally and applied widely in image quality assessment (IQA). However, using SSIM to evaluate contrast-distorted images from TID2013 and CSIQ databases is low effective. In this study, the authors improve SSIM for contrast-distorted images by combining it with the contrast sensitivity characteristics of human visual system (HVS). In the improved method, first, they combine the visual characteristics to propose a model that HVS perceives the real image. Then, this model is used to eliminate the visual redundancy of real images. Afterwards, the perceived images are evaluated using SSIM. Furthermore, 241 contrast-distorted images from TID2013 and CSIQ databases were used in experiments. The results have shown that in comparison with SSIM scores, the scores obtained by the improved SSIM are more consistent with the subjective assessment scores. Moreover, the Pearson linear correlation coefficient and Spearman rank order correlation coefficient between subjective and objective scores are averagely improved by 12.83 and 22.78%, respectively. In addition, the assessment accuracy of the improved SSIM is better than that of five commonly used IQA metrics. Also, it has an excellent generalisation performance. These results show that the assessment performance of the improved SSIM is effectively enhanced.

References

1. 1)
  - 13. ‘The CSIQ image database’. Available at http://vision.okstate.edu/?loc=csiq, accessed 20 May 2017.
2. 2)
  - 7. Wang, Z., Li, Q.: ‘Information content weighting for perceptual image quality assessment’, IEEE Trans. Image Process., 2011, 20, (5), pp. 1185–1198.
3. 3)
  - 4. Chandler, D.M., Hemami, S.S.: ‘VSNR: a wavelet-based visual signal-to-noise ratio for natural images’, IEEE Trans. Image Process., 2007, 16, (9), pp. 2284–2298.
4. 4)
  - 20. Yao, J.C.: ‘Measurements of human vision contrast sensitivity to opposite colors using a CRT display’, Chin. Sci. Bull., 2011, 56, (23), pp. 2425–2432.
5. 5)
  - 23. Zhang, F., Bull, D.R.: ‘Quality assessment methods for perceptual video compression’. Proc. 20th IEEE Int. Conf. Image Processing (ICIP), Melbourne, VIC, Australia, 15–18 September 2013, pp. 39–43.
6. 6)
  - 16. Sheikh, H.R., Sabir, M.F., Bovik, A.C.: ‘A statistical evaluation of recent full reference image quality assessment algorithms’, IEEE Trans. Image Process., 2006, 15, (11), pp. 3440–3451.
7. 7)
  - 22. Nadenau, M.: ‘Integration of human color vision models into high quality image compression’. PhD thesis, Swiss Federal Institute of Technology in Lausanne, Switzerland, 2000.
8. 8)
  - 12. Ponomarenko, N., Jin, L., Ieremeiev, O., et al: ‘Image database TID2013: peculiarities, results and perspectives’, Signal Process. Image Commun., 2015, 30, (1), pp. 57–77.
9. 9)
  - 21. Barten, P.: ‘Evaluation of subjective image quality with the square-root integral method’, J. Opt. Soc. Am. A, 1990, 7, (10), pp. 2024–2031.
10. 10)
  - 19. Li, C., Bovik, A.C.: ‘Content-weighted video quality assessment using a three-component image model’, J. Electron. Imaging, 2010, 29, (1), pp. 143–153.
11. 11)
  - 9. Bae, S.H., Kim, M.: ‘A novel SSIM index for image quality assessment using a new luminance adaptation effect model in pixel intensity domain’. IEEE Int. Conf. Visual Communications and Image Processing (VCIP), Singapore, 13–16 December 2015, pp. 1–4.
12. 12)
  - 8. Fang, Y., Ma, K., Wang, Z., et al: ‘No-reference quality assessment of contrast-distorted images based on natural scene statistics’, IEEE Signal Process. Lett., 2015, 22, (7), pp. 838–842.
13. 13)
  - 6. Xue, W., Zhang, L., Mou, X., et al: ‘Gradient magnitude similarity deviation: a highly efficient perceptual image quality index’, IEEE Trans. Image Process., 2014, 23, (2), pp. 684–695.
14. 14)
  - 15. ‘LIVE image quality assessment database release 2’. Available at http://live.ece.utexas.edu/research/quality, accessed 20 May 2017.
15. 15)
  - 17. Mendi, E.: ‘Image quality assessment metrics combining structural similarity and image fidelity with visual attention’, J. Intell. Fuzzy Syst., 2015, 28, (3), pp. 1039–1046.
16. 16)
  - 10. Wang, Z., Li, L., Wu, S., et al: ‘A new image quality assessment algorithm based on SSIM and multiple regressions’, Int. J. Signal Process. Image Process. Pattern Recognit., 2015, 8, (11), pp. 221–230.
17. 17)
  - 2. Qin, M., Lv, X., Chen, X., et al: ‘Hybrid NSS features for no-reference image quality assessment’, IET Image Process., 2017, 11, (6), pp. 443–449.
18. 18)
  - 1. Wu, Q., Li, H., Meng, F., et al: ‘Blind image quality assessment based on multichannel feature fusion and label transfer’, IEEE Trans. Circuits Syst.Video Technol., 2016, 26, (3), pp. 425–440.
19. 19)
  - 14. Larson, E.C., Chandler, D.M.: ‘Most apparent distortion: full-reference image quality assessment and the role of strategy’, J. Electron. Imaging, 2010, 19, (1), pp. 143–153.
20. 20)
  - 5. Zhang, L., Zhang, L., Mou, X., et al: ‘FSIM: a feature similarity index for image quality assessment’, IEEE Trans. Image Process., 2011, 20, (8), pp. 2378–2386.
21. 21)
  - 11. ‘Tampere image database 2013 TID2013, version 1.0’. Available at http://www.ponomarenko.info/tid2013.htm, accessed 20 May 2017.
22. 22)
  - 3. 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.
23. 23)
  - 18. Gu, K., Wang, S., Zhai, G., et al: ‘Content-weighted mean-squared error for quality assessment of compressed images’, Signal Image Video Process., 2016, 10, (5), pp. 803–810.

Improved SSIM IQA of contrast distortion based on the contrast sensitivity characteristics of HVS

References

Related content