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Research Articles

A complete no-reference image quality assessment method based on local feature

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Pages 165-176 | Received 30 Jun 2018, Accepted 23 Apr 2019, Published online: 05 May 2019

References

  • Bahrami, K. and Kot, A.C., 2014. A fast approach for no-reference image sharpness assessment based on maximum local variation. IEEE Signal Processing Letter, 21 (6), 751–755. doi:10.1109/LSP.2014.2314487
  • Chen, G., Zhu, F., and Heng, P.A., 2015. An efficient statistical method for image noise level estimation. In: 2015 IEEE International Conference on Computer Vision (ICCV 2015), 7–13 December 2015 Santiago, Chile. IEEE, 477–485. doi:10.1177/1753193414567425
  • Donoho, D.L. and Johnstone, J.M., 1994. Ideal spatial adaptation by wavelet shrinkage. Biometrika, 81 (3), 425–455. doi:10.1093/biomet/81.3.425
  • Golestaneh, S.A. and Karam, L.J., 2015. Reduced-reference quality assessment based on the entropy of DNT coefficients of locally weighted gradients. In: 22nd IEEE International Conference on Image Processing (ICIP 2015), 27–30 September 2015 Quebec city, Canada. IEEE, 4117–4120.
  • Hassen, R., Wang, Z., and Salama, M., 2013. Image sharpness assessment based on local phase coherence. IEEE Transactions on Image Processing, 22 (7), 2798–2810. doi:10.1109/TIP.2013.2251643
  • Larson, E.C. and Chandler, D., 2010. Most apparent distortion: fullreference image quality assessment and the role of strategy. Journal of Electronics Imaging, 19 (1), 011006-1-011006-21. doi:10.1117/1.3267105
  • Li, C., et al., 2011. No-reference blur index using blur comparisons. Electronic Letter, 47 (17), 962–963. doi:10.1049/el.2011.0921
  • Liang, H. and Weller, D.S., 2016. Comparison-based image quality assessment for selecting image restoration parameters. IEEE Transactions on Image Processing, 25 (11), 5118–5130. doi:10.1109/TIP.2016.2601783
  • Liu, H. and Heynderickx, I., 2011. Visual attention in objective image quality assessment: based on eye-tracking data. IEEE Transactions on Circuits and Systems for Video Technology, 21 (7), 971–982. doi:10.1109/TCSVT.2011.2133770
  • Ma, L., et al., 2016. No-referencere targeted image quality assessment based on pairwise rank learning. IEEE Transactions on Multimedia, 18 (11), 2228–2237. doi:10.1109/TMM.2016.2614187
  • Mittal, A., Moorthy, A.K., and Bovik, A.C., 2012. No-reference image quality assessment in the spatial domain. IEEE Transactions on Image Processing, 21 (12), 4695–4708. doi:10.1109/TIP.2012.2214050
  • Mittal, A., Muralidhar, G.S., and Bovik, A.C., 2013. Making a ‘completely blind’ image quality analyzer. IEEE Signal Processing Letter, 20 (3), 209–212. doi:10.1109/LSP.2012.2227726
  • Moorthy, A.K. and Bovik, A.C., 2011. Blind image quality assessment: from natural scene statistics to perceptual quality. IEEE Transactions on Image Processing, 20 (12), 3350–3364. doi:10.1109/TIP.2011.2147325
  • Ponomarenko, N., et al., 2015. Image database TID2013: preculiarites, results and perspectives. Signal Processing: Image Communication, 30, 57–77.
  • Saad, M., Bovik, A.C., and Charrier, C., 2012. Blind image quality assessment: A natural scene statistics approach in the DCT domain. IEEE Transactions on Image Processing, 21 (8), 3339–3352. doi:10.1109/TIP.2012.2191563
  • Saha, A. and Wu, Q.M., 2015. Utilizing image scales towards totally training free blind image quality assessment. IEEE Transactions on Image Processing, 24 (6), 1879–1892. doi:10.1109/TIP.2015.2411436
  • Sazzad, Z.M.P., Kawayoke, Y., and Horita, Y., 2007. Spatial features based no reference image quality assessment forJPEG2000. In: 2007 IEEE International Conference on Image Processing (ICIP 2007), 16–19 September 2007 San Antonio, TX, U.S.A. IEEE, 517–520.
  • Sheikh, H.R., Wang, L.C.Z., and Bovik, A.C., 2016. Data form: LIVE image quality assessment database. Available from: http://live.ece.utexas.edu/research/quality [Accessed 15 December 2017].
  • Wang, Z., et al., 2004. Image quality assessment: from error visibility to structural similarity. IEEE Transactions on Image Processing, 13 (4), 600–612. doi:10.1109/TIP.2003.819861
  • Wang, Z. and Bovik, A.C., 2006. Modern image quality assessment. New York: Morgan and Claypool Publishers.
  • Wu, J., et al., 2013. Reduced-reference image quality assessment with visual information fidelity. IEEE Transactions on Multimedia, 15 (7), 1700–1705. doi:10.1109/TMM.2013.2266093
  • Wu, Q., Wang, Z., and Li, H., 2015. A highly efficient method for blind image quality assessment. In: 22nd IEEE International Conference on Image Processing (ICIP2015), 27–30 September 2015 Quebec City, Canada. IEEE, 339–343.
  • Xue, W.F., et al., 2014. Gradient magnitude similarity deviation: a highly efficien tperceptual image quality index. IEEE Transactions on Image Processing, 23 (2), 684–695. doi:10.1109/TIP.2013.2293423

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