No-reference image quality assessment based on spatial and spectral entropies

Signal Processing: Image Communication

Volumn 29, Issue 8, 2014, Pages 856-863

  Liu, Lixiong ^a   Liu, Bao ^a   Huang, Hua ^a   Bovik, Alan Conrad ^b  

^a BEIJING INSTITUTE OF TECHNOLOGY   (China)

^b The University of Texas at Austin   (United States)

Author keywords

Image quality assessment; No reference; Spatial entropy; Spectral entropy; Support vector machine

Indexed keywords

DATABASE SYSTEMS; ENTROPY; IMAGE MATCHING; SPECTRUM ANALYSIS; SUPPORT VECTOR MACHINES;

IMAGE QUALITY ASSESSMENT; IMAGE QUALITY ASSESSMENT (IQA); NO REFERENCES; NO-REFERENCE IMAGE QUALITY ASSESSMENTS; QUALITY ASSESSMENT; QUALITY PREDICTION; SPATIAL ENTROPY; SPECTRAL ENTROPY;

IMAGE QUALITY;

EID: 84923320061     PISSN: 09235965     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.image.2014.06.006     Document Type: Article

References (29)

1. W. Lin, and C.-C.J. Kuo Perceptual visual quality metrics: a survey J. Vis. Commun. Image Represent. 22 4 2011 297 312
2. X. Gao, W. Lu, D. Tao, X. Li, Image quality assessment and human visual system, in: Proceedings of SPIE 7744, Visual Communications and Image Processing 2010, August 2010, 7744, pp. 77440Z-1-77440Z-10.
3. Z.M.P. Sazzad, Y. Kawayoke, and Y. Horita No-reference image quality assessment for jpeg2000 based on spatial features Signal Process. Image Commun. 23 4 2008 257 268
4. X. Zhu, P. Milanfar, A no-reference sharpness metric sensitive to blur and noise, International Workshop on Quality of Multimedia Experience, San Diego, CA, July 2009, pp. 64-69.
5. M.J. Chen, and A.C. Bovik No-reference image blur assessment using multiscale gradient EURASIP J. Image Video Process. 1 2011 1 11
6. Z. Wang, A.C. Bovik, B.L. Evans, Blind measurement of blocking artifacts in images, in: Proceedings of the IEEE International Conference on Image Processing, September 2000, pp. 981-984.
7. H.R. Sheikh, A.C. Bovik, and L.K. Cormack No-reference quality assessment using natural scene statistics: JPEG2000 IEEE Trans. Image Process. 14 11 2005 1918 1927
8. A.K. Moorthy, and A.C. Bovik A two-step framework for constructing blind image quality indices IEEE Signal Process. Lett. 17 5 2010 513 516
9. 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 3350 3364
10. M. 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 3339 3352
11. A. Mittal, A.K. Moorthy, and A.C. Bovik No-reference image quality assessment in the spatial domain IEEE Trans. Image Process. 1 1 2012 4695 4708
12. L. Liu, H. Dong, H. Huang, and A.C. Bovik No-reference image quality assessment in curvelet domain Signal Process. Image Commun. 29 4 2014 494 505
13. J. Sponring, The entropy of scale-space, in: Proceedings of the 13th International Conference on Pattern Recognition, vol. 1, August 1996, pp. 900-904.
14. 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 2117 2128
15. H.R. Sheikh, and A.C. Bovik Image information and visual quality IEEE Trans. Image Process. 15 2 2006 430 444
16. Q. Hu, Z.X. Xie, Z.F. Wang, Y.H. Liu, Constructing NR-IQA function based on product of information entropy and contrast, in: International Symposium on Information Science and Engineering, vol. 2, December 2008, pp. 548-550.
17. 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 600 612
18. A.K. Moorthy, and A.C. Bovik Visual importance pooling for image quality assessment IEEE J. Select. Top. Signal Process. 3 2 2009 193 201
19. M. Narwaria, and W. Lin Objective image quality assessment based on support vector regression IEEE Trans. Neural Netw. 21 3 2010 515 519
20. M. Narwaria, and W. Lin SVD-based quality metric for image and video using machine learning IEEE Trans. Syst. Man Cybern. Part B (Cybern.) 42 2 2012 347 364
21. P. Carrai, I. Heynderickx, P. Gastaldo, R. Zunino, Image quality assessment by using neural networks, in: Proceedings of IEEE International Symposium on Circuits and Systems, vol. 5, 2002, pp. V-253-V-256.
22. A. Bouzerdoum, A. Havstad, A. Beghdadi, Image quality assessment using a neural network approach, in: Proceedings of the 4th IEEE International Symposium on Signal Processing and Information Technology, 2004, pp. 330-333.
23. B. Schölkopf, A.J. Smola, R.C. Williamson, and P.L. Bartlett New support vector algorithms Neural Comput. 12 5 2000 1207 1245
24. C.J.C. Burges A tutorial on support vector machines for pattern recognition Data Min. Knowl. Discov. 2 2 1998 121 167
25. C.C. Chang, C.J. Lin. LIBSVM: a library for support vector machines. Available from: http://www.csie.ntu.edu.tw/~cjlin/libsvm/, 2001.
26. H.R. Sheikh, Z. Wang, L. Cormack, A.C. Bovik. LIVE image quality assessment database release 2. Available from: http://live.ece.utexas.edu/ research/quality, 2006.
27. H.R. Sheikh, M.F. Sabir, and A.C. Bovik A statistical evaluation of recent full reference image quality assessment algorithms IEEE Trans. Image Process. 15 11 2006 3440 3451
28. 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 98 117
29. N. Ponomarenko, V. Lukin, A. Zelensky, K. Egiazarian, M. Carli, and F. Battisti TID 2008 a database for evaluation of full-reference visual quality assessment metrics Adv. Mod. Radioelectron. 10 2009 30 45