Objective image quality assessment: a survey

International Journal of Computer Mathematics

Volumn 91, Issue 11, 2014, Pages 2374-2388

  He, Lihuo ^a   Gao, Fei ^a   Hou, Weilong ^a   Hao, Lei ^a  

^a XIDIAN UNIVERSITY   (China)

Author keywords

human visual system; image quality assessment; machine learning; perceptual quality; visual information

Indexed keywords

LEARNING SYSTEMS; PHILOSOPHICAL ASPECTS;

COMPUTATIONAL MODEL; HUMAN VISUAL SYSTEM; IMAGE QUALITY ASSESSMENT; IMAGE QUALITY ASSESSMENT (IQA); OBJECTIVE IMAGE QUALITY ASSESSMENT; PERCEPTUAL QUALITY; STATE OF THE ART; VISUAL INFORMATION;

IMAGE QUALITY;

EID: 84912143698     PISSN: 00207160     EISSN: 10290265     Source Type: Journal    
DOI: 10.1080/00207160.2013.816415     Document Type: Article

References (90)

1. A. Borji and L. Itti, State-of-the-art in visual attention modeling, IEEE Trans. Pattern Anal. Mach. Intell. 35(1) (2013), pp. 185–207. doi: 10.1109/TPAMI.2012.89
2. A.P. Bradley, A wavelet difference predictor, IEEE Trans. Image Process. 8(5) (1999), pp. 717–730. doi: 10.1109/83.760338
3. R.W. Buccigrossi and E.P. Simoncelli, Image compression via joint statistical characterization in the wavelet domain, IEEE Trans. Image Process. 8(12) (1999), pp. 1688–1701. doi: 10.1109/83.806616
4. P.L. Callet and F. Autrusseau, Subjective quality assessment-IVC database, 2006. Available at http://www.irccyn.ec-nantes.fr/ivcdb/.
5. D.M. Chandler and S.S. Hemami. A57 database, 2007. Available at http://foulard.ece.cornell.edu/dmc27/vsnr/a57_db.zip.
6. D.M. Chandler and S.S. Hemami, VSNR: A wavelet-based visual signal-to-noise ratio for natural images, IEEE Trans. Image Process. 16(9) (2007), pp. 2284–2298. doi: 10.1109/TIP.2007.901820
7. U.M. Chaskar, M.S. Sutaone, N.S. Shah, and T. Jaison, Iris image quality assessment for biometric application, Int. J. Comput. Sci. Issues 3(3) (2012), pp. 474–478.
8. E. Christophe, D. Léger, and C. Mailhes, Quality criteria benchmark for hyperspectral imagery, IEEE Trans. Geosci. Remote Sens. 43(9) (2005), pp. 2103–2114. doi: 10.1109/TGRS.2005.853931
9. A. Ciancio, A.L.N. Targino da Costa, E.A.B. da Silva, A. Said, R. Samadani, and P. Obrador, No-reference blur assessment of digital pictures based on multifeature classifiers, IEEE Trans. Image Process. 20(1) (2011), pp. 64–75. doi: 10.1109/TIP.2010.2053549
10. S. Daly, The visible difference predictor: An algorithm for the assessment of image fidelity, in Digital Images and Human Vision, A.B. Watson, ed., The MIT Press, Cambridge, MA, 1993, pp. 179–206.
11. N. Damera-Venkata, D.T. Kite, S.W. Geisler, L.B. Evans, and A.C. Bovik, Image quality assessment based on a degradation model, IEEE Trans. Image Process. 4(4) (2000), pp. 636–650. doi: 10.1109/83.841940
12. U. Engelke, H.-J. Zepernick, and M. Kusuma, Wireless imaging quality database, 2010. Available at http://www.bth.se/tek/rcg.nsf/pages/wiq-db.
13. U. Engelke, H. Kaprykowsky, H.-J. Zepernick, and P. Ndjiki-Nya, Visual attention in quality assessment, IEEE Signal Process. Mag. 28(6) (2011), pp. 50–59. doi: 10.1109/MSP.2011.942473
14. R. Ferzli and L.J. Karam, A no-reference objective image sharpness metric based on the notion of just noticeable blur (JNB). IEEE Trans. Image Process. 18(4) (2009), pp. 717–728. doi: 10.1109/TIP.2008.2011760
15. Final report from the Video Quality Experts Group (VQEG) on the validation of objective models of video quality assessment, Phase II VQEG, 2003. Available at http://www.vqeg.org/
16. H. Fronthaler, K. Kollreider, and J. Bigun, Automatic image quality assessment with application in biometrics, Computer Vision and Pattern Recognition Workshop, New York, NY, USA, 2006.
17. X.B. Gao, T. Wang, and J. Li, A content-based image quality metric, in RSFDGrC2005, Regina, Canada, Lecture Notes in Artificial Intelligence, Vol. 3642, D. Slezak, J. Yao, J. F. Peters, W. Ziarko, X. Hu, eds., Springer, Berlin, Heidelberg, 2005, pp. 231–240.
18. X. Gao, W. Lu, X. Li, and D. Tao, Wavelet-based contourlet in quality evaluation of digital images, Neurocomputing 72(12) (2008), pp. 378–385. doi: 10.1016/j.neucom.2007.12.031
19. X. Gao, W. Lu, D. Tao, and X. Li, Image quality assessment based on multiscale geometric analysis, IEEE Trans. Image Process. 18(7) (2009), pp. 1409–1423. doi: 10.1109/TIP.2009.2018014
20. F. Gao, X. Gao, W. Lu, D. Tao, and X. Li, An image quality assessment metric with no-reference using hidden Markov tree model, in Proceedings of SPIE, Visual Communications and Image Processing (VCIP), Huangshan, China, Vol. 7744, H. Li, F. Wu, B. Girod, S. Li, and G. Wei, eds., SPIE, Bellingham, WA, 2010, pp. 774410-1–774410-7.
21. F. Gao, X. Gao, D. Tao, X. Li, L. He, and W. Lu, Universal NR-IQA metrics based on local dependency, Proceedings of Asian Conference on Pattern Recognition (ACPR), Beijing, China, 2011, pp. 298–302.
22. F. Gao, D. Tao, X. Li, X. Gao, and L. He, Local structure divergence index for image quality assessment, ICONIP, Doha, Qatar, Part V, LNCS, Vol. 7667, 2012, pp. 337–344.
23. J.C. García álvarez, Image quality assessment based on full-referenced perceptual measures, Ph.D. thesis, Universidad Nacional de Colombia, 2011.
24. I.P. Gunawan and M. Ghanbari, Image quality assessment based on harmonics gain/loss information, Proceeding of IEEE International Conference on Image Processing, ICIP 2005, Genoa, Italy, IEEE, 2005, pp. 29–32.
25. L. He, W. Lu, X. Gao, D. Tao, and X. Li, A novel metric based on MCA for image quality, Int. J. Wavelets Multiresolution Inf. Process. 9(5) (2011), pp. 743–757. doi: 10.1142/S0219691311004298
26. L. He, X. Gao, W. Lu, X. Li, and D. Tao, Image quality assessment based on S-CIELAB model, Signal Image Video Process. 5(3) (2011), pp. 283–290. doi: 10.1007/s11760-010-0200-x
27. L. He, D. Wang, X. Li, D. Tao, X. Gao, and F. Gao, Color fractal structure model for reduced-reference colorful image quality assessment, ICONIP, Doha, Qatar, Part II, LNCS, Vol. 7664, 2012, pp. 401–408.
28. L. He, D. Tao, X. Li, and X. Gao, Sparse representation for blind image quality assessment, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Providence, RI, USA, 2012, pp. 1146–1153.
29. Y. Horita, K. Shibata, Y. Kawayoke, and Z.M.P. Sazzad, MICT image quality evaluation database, 2000. Available at http://mict.eng.u-toyama.ac.jp/mictdb.html.
30. Y. Jia, W. Lin, and A.A. Kassim, Estimating just-noticeable distortion for video, IEEE Trans. Circuits Syst. Video Technol. 16(7) (2006), pp. 820–829. doi: 10.1109/TCSVT.2006.877397
31. Y. Ke, X. Tang, and F. Jing, The design of high-level features for photo quality assessment, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), New York, NY, USA, 2006, pp. 419–426.
32. S.A. Klein, T. Carney, L. Barghout-Stein, and C.W. Tyler, Seven models of masking, Proceedings of SPIE Human Vision and Electronic Imaging II, Vol. 3016, San Jose, CA, 1997, pp. 13–24.
33. T.M. Kusuma and H.J. Zepernick, A reduced-reference perceptual quality metric for in-service image quality assessment, Proceedings of Joint First Workshop on Mobile Future and IEEE Symposium on Trends in Communications, Bratislava, Slovakia, 2003, pp. 71–74. doi: 10.1109/TIC.2003.1249092
34. E.C. Larson and D.M. Chandler, Categorical image quality (CSIQ) database, 2009. Available at http://vision.okstate.edu/csiq.
35. E.C. Larson and D.M. Chandler, Most apparent distortion: Full-reference image quality assessment and the role of strategy, J. Electron. Imaging 19(1) (2010), pp. 011006-1–011006-21.
36. C. Li, and T. Chen, Aesthetic visual quality assessment of paintings, IEEE J. Sel. Top. Sign. Process. 3(2) (2009), pp. 236–252. doi: 10.1109/JSTSP.2009.2015077
37. Q. Li and Z. Wang, RR image quality assessment using divisive normalization-based image representation, IEEE J. Sel. Top. Sign. Process. 3(2) (2009), pp. 202–211. doi: 10.1109/JSTSP.2009.2014497
38. X. Li, D. Tao, X. Gao, and W. Lu, A natural image quality evaluation metric, Sign. Process. 89(4) (2009), pp. 548–555. doi: 10.1016/j.sigpro.2008.10.007
39. X. Li, L. He, X. Gao, W. Lu, and D. Tao, A novel image quality metric based on morphological component analysis, Proceedings of IEEE International Conference on Systems, Man, and Cybernetics (SMC), Istanbul, Turkey, 2010, pp. 1449–1454.
40. C. Li, A.C. Bovik, and X. Wu, Blind image quality assessment using a general regression neural network, IEEE Trans. Neural Networks 22(5) 2011, pp. 793–799. doi: 10.1109/TNN.2011.2120620
41. W. Lin, and C.-C. Jay Kuo, Perceptual visual quality metrics: A survey, J. Visual Commun. Image Represent. 22(4) (2011), pp. 297–312. doi: 10.1016/j.jvcir.2011.01.005
42. W. Lin, L. Dong, and P. Xue, Visual distortion gauge based on discrimination of noticeable contrast changes, IEEE Trans. Circuits Syst. Video Technol. 15(7) (2005), pp. 900–909. doi: 10.1109/TCSVT.2005.848345
43. A. Liu, W. Lin, and M. Narwaria, Image quality assessment based on gradient similarity, IEEE Trans. Image Process. 21(4) (2012), pp. 1500–1512. doi: 10.1109/TIP.2011.2175935
44. W. Lu, X. Gao, D. Tao, and X. Li, A wavelet-based image quality assessment method, Int. J. Wavelets Multiresolution Inf. Process. 6(4) (2008), pp. 541–551. doi: 10.1142/S0219691308002501
45. W. Lu, X. Gao, D. Tao, and X. Li, An image quality assessment metric based on contourlet, Proceedings of IEEE International Conference on Image Processing, San Diego, CA, USA, 2008, pp. 12–15.
46. W. Lu, K. Zeng, D. Tao, Y. Yuan, and X. Gao, No-reference image quality assessment in contourlet domain, Neurocomputing 73(12) (2010), pp. 784–794. doi: 10.1016/j.neucom.2009.10.012
47. W. Luo, X. Wang, and X. Tang, Content-based photo quality assessment, Proceedings of IEEE International Conference on Computer Vision (ICCV), Barcelona, Brazil, 2011, pp. 2206–2213.
48. L. Ma, S. Li, F. Zhang, and K.N. Ngan, Reduced-reference image quality assessment using reorganized DCT-based image representation, IEEE Trans. Multimedia 13(4) (2011), pp. 824–829. doi: 10.1109/TMM.2011.2109701
49. J.L. Mannos and D.J. Sakrison, The effects of a visual fidelity criterion on the encoding of images, IEEE Trans. Inf. Theory 20(4) (1974), pp. 525–536. doi: 10.1109/TIT.1974.1055250
50. Ć. Martin, R. Herzog, R. Mantiuk, K. Myszkowski, and H.-P. Seidel, New measurements reveal weaknesses of image quality metrics in evaluating graphics artifacts, ACM Trans. Graphics 31(6) (2012), pp. 1–10.
51. M.G. Martinia, C.T.E.R. Hewagea, and B. Villarinia, Image quality assessment based on edge preservation, Signal Process. Image Commun. 27(8) (2012), pp. 875–882. doi: 10.1016/j.image.2012.01.012
52. P. Marziliano, F. Dufaux, S. Winkler, and T. Ebrahimi, Perceptual blur and ringing metrics: Application to JPEG2000, Signal Process. Image Commun. 19(2) (2004), pp. 163–172.
53. T. Mei, X.-S. Hua, C.-Z. Zhu, H.-Q. Zhou, and S. Li, Home video visual quality assessment with spatiotemporal factors, IEEE Trans. Circuits Syst. Video Technol. 17(6) (2007), pp. 699–706. doi: 10.1109/TCSVT.2007.896640
54. Methodology for the subjective assessment of the quality of television pictures, Recommendation ITU-R Rec. BT. 500-11
55. A. Mittal, A.K. Moorthy, and A.C. Bovik, No-reference image quality assessment in the spatial domain, IEEE Trans. Image Process. 21(12) (2012), pp. 4695–4708. doi: 10.1109/TIP.2012.2214050
56. A. Mittal, G.S. Muralidhar, J. Ghosh, and A.C. Bovik, Blind image quality assessment without human training using latent quality factors, IEEE Signal Process. Lett. 19(2) (2012), pp. 75–78. doi: 10.1109/LSP.2011.2179293
57. M. Miyahara, K. Kotani, and V.R. Algazi, Objective picture quality scale for image coding, IEEE Trans. Commun. 46(9) (1998), pp. 1215–1225. doi: 10.1109/26.718563
58. A.K. Moorthy and A.C. Bovik, A two-step framework for constructing blind image quality indices, IEEE Signal Process. Lett. 17(6) (2010), pp. 513–516. doi: 10.1109/LSP.2010.2043888
59. A.K. Moorthy and A.C. Bovik, Blind image quality assessment: From natural scene statistics to perceptual quality, IEEE Trans. Image Process. 20(12) (2011), pp. 3350–3364. doi: 10.1109/TIP.2011.2147325
60. T. Phan, P. Vu, and D.M. Chandler, On the use of image quality estimators for improved JPEG2000 coding, 46th Asilomar Conference on Signals, Systems and Computers, Pacific Grove, CA, USA, 2012.
61. N. Ponomarenko, V. Lukin, A. Zelensky, K. Egiazarian, M. Carli, and F. Battisti, TID2008 – a database for evaluation of full-reference visual quality assessment metrics, Adv. Modern Radioelectron. 10 (2009), pp. 30–45.
62. M.A. Saad, A.C. Bovik, and C. Charrier, A DCT statistics based blind image quality index, IEEE Signal Process. Lett. 17(6) (2011), pp. 583–586. doi: 10.1109/LSP.2010.2045550
63. M.A. Saad, A.C. Bovik, and C. Charrier, Blind image quality assessment: A natural scene statistics approach in the DCT domain, IEEE Trans. Image Process. 21(8) (2012), pp. 3339–3352. doi: 10.1109/TIP.2012.2191563
64. R.J. Safranek and J.D. Johnston, A perceptually tuned sub-band image coder with image dependent quantization and post-quantization data compression, Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing, Glasgow, UK, 1989, pp. 1945–1948.
65. H.R. Sheikh and A.C. Bovik, Image information and visual quality, IEEE Trans. Image Process. 15(2) (2006), pp. 430–444. doi: 10.1109/TIP.2005.859378
66. H.R. Sheikh, Z. Wang, L. Cormack, and A.C. Bovik, Live image quality assessment database release 2 (LIVE II), 2003. Available at http://live.ece.utexas.edu/research/quality.
67. H.R. Sheikh, A.C. Bovik, and L. Cormack, No-reference quality assessment using natural scene statistics: JPEG2000, IEEE Trans. Image Process. 14(11) (2005), pp. 1918–1927.
68. H.R. Sheikh, A.C. Bovik, and G. de Veciana, An information fidelity criterion for image quality assessment using natural scene statistics, IEEE Trans. Image Process. 14(12) (2005), pp. 2117–2128. doi: 10.1109/TIP.2005.859389
69. J. Shen, Q. Li, and G. Erlebacher, Hybrid no-reference natural image quality assessment of noisy, blurry, JPEG2000, and JPEG images, IEEE Trans. Image Process. 20(8) (2012), pp. 2089–2098.
70. A. Shnayderman, A. Gusev, and A.M. Eskicioglu, An SVD-based grayscale image quality measure for local and global assessment, IEEE Trans. Image Process. 15(2) (2006), pp. 422–429. doi: 10.1109/TIP.2005.860605
71. R. Soundararajan and A.C. Bovik, RRED indices: Reduced reference entropic differencing for image quality assessment, IEEE Trans. Image Process. 21(2) (2012), pp. 517–526. doi: 10.1109/TIP.2011.2166082
72. A. Srivastava, A. Lee, E. Simoncelli, and S. Zhu, On advances in statistical modeling of natural images, J. Math. Imaging Vision 18(1) (2003), pp. 17–33. doi: 10.1023/A:1021889010444
73. H. Tang, N. Joshi, and A. Kapoor, Learning a blind measure of perceptual image quality, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Colorado Springs, USA, 2011, pp. 305–312.
74. D. Tao, X. Li, W. Lu, and X. Gao, Reduced-reference IQA in contourlet domain, IEEE Trans. Syst. Man Cybern. Part B Cybern. 39(6) (2009), pp. 1623–1627. doi: 10.1109/TSMCB.2009.2021951
75. VQEG, Validation of reduced-reference and no-reference objective models for standard definition television, Phase I, 2009. Available at http://www.vqeg.org/.
76. Z. Wang, Applications of objective image quality assessment methods, IEEE Signal Process. Mag. 28(6) (2011), pp. 137–142. doi: 10.1109/MSP.2011.942295
77. Z. Wang and A.C. Bovik, Modern Image Quality Assessment, Morgan and Claypool Publishing Company, New York, 2006.
78. Z. Wang and A.C. Bovik, Mean squared error: Love it or leave it? – A new look at signal fidelity measures, IEEE Signal Process. Mag. 26(1) (2009), pp. 98–117. doi: 10.1109/MSP.2008.930649
79. Z. Wang and Q. Li, Information content weighting for perceptual image quality assessment, IEEE Trans. Image Process. 20(5) (2011), pp. 1185–1198. doi: 10.1109/TIP.2010.2092435
80. Z. Wang and E.P. Simoncelli, Reduced-reference image quality assessment using a wavelet-domain natural image statistic model, in Proceedings of SPIE Human Vision and Electronic Imaging X, San Jose, CA, USA, Vol. 5666, B. E. Rogowitz, T.N. Pappas, S.J. Daly, eds., SPIE and IS&T, 2005, pp. 149–159.
81. Z. Wang, A.C. Bovik, and B.L. Evans, Blind measurement of blocking artifacts in images, Proceedings of the IEEE International Conference on Image Processing, Vancouver, BC, Canada, Vol. 3, IEEE, 2000, pp. 981–984.
82. Z. Wang, A.C. Bovik, and L. Lu, Why is image quality assessment so difficult? Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, Orlando, FL, USA, Vol. 4, IEEE, 2002, pp. 3313–3316.
83. Z. Wang, A.C. Bovik, H.R. Sheikh, and E.P. Simoncelli, Image quality assessment: From error visibility to structural similarity, IEEE Trans. Image Process. 13(4) (2004), pp. 600–612. doi: 10.1109/TIP.2003.819861
84. Z. Wang, G. Wu, H.R. Sheikh, E.P. Simoncelli, E. Yang, and A.C. Bovik, Quality-aware images, IEEE Trans. Image Process. 15(6) (2006), pp. 1680–1689. doi: 10.1109/TIP.2005.864165
85. Y. Wang, T. Jiang, S. Ma, and W. Gao, Novel spatio-temporal structural information based video quality metric, IEEE Trans. Circuits Syst. Video Technol. 22(7) (2012), pp. 989–998. doi: 10.1109/TCSVT.2012.2186745
86. A.B. Watson, G.Y. Yang, J.A. Solomon, and J. Villasenor, Visibility of wavelet quantization noise, IEEE Trans. Image Process. 6(8) (1997), pp. 1164–1175. doi: 10.1109/83.605413
87. S. Winkler, Analysis of public image and video databases for quality assessment, IEEE J. Sel. Top. Sign. Process. 6(6) (2012), pp. 616–625. doi: 10.1109/JSTSP.2012.2215007
88. P. Ye, J. Kumar, L. Kang, and D. Doermann, Unsupervised feature learning framework for no-reference image quality assessment, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Providence, RI, USA, 2012, pp. 1098–1105.
89. X. Zhang, W. Lin, and P. Xue, Just-noticeable difference estimation with pixels in images, J. Visual Commun. Image Represent. 19(1) (2008), pp. 30–41. doi: 10.1016/j.jvcir.2007.06.001
90. L. Zhang, L. Zhang, X. Mou, and D. Zhang, FSIM: A feature similarity index for image quality assessment, IEEE Trans. Image Process. 20(8) (2011), pp. 2378–2386. doi: 10.1109/TIP.2011.2109730