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Effectiveness of Image Quality Assessment Indexes on
Detection of Structural and Nonstructural Distortions
Michel Alves dos Santos ∗
January, 2014
Abstract
The main objective of this theme is to provide a study of effectiveness
of the main image quality indexes in relation to the detection of distor-
tions introduced after processes of acquisition, compression, filtering or
sampling, as well as introducing a range of "admissibility" of distortions
and degradation classes (like classes of noise, classes of blocking, classes
of compression, classes of fusion/blending, classes of watermarking, etc.).
Keywords: image processing, contrast distortion, image distortion, im-
age processing applications, loss of correlation, luminance distortion, im-
age quality index, distortion measurement, dynamic range, image quality,
mathematical models, human visual system, signal to noise ratio
∗Michel Alves dos Santos - Alves, M. - malves@cos.ufrj.br - http://www.michelalves.com. MSc. Candidate in
Computer Graphics, Image Processing and Computer Vision. http://www.lcg.ufrj.br - Laboratory of Computer
Graphics - LCG. Graduate Program in Systems Engineering and Computing (PESC). Alberto Luiz Coimbra In-
stitute for Graduate Studies and Research in Engineering - COPPE. Federal University of Rio de Janeiro (UFRJ -
http://www.ufrj.br), Brazil - Rio de Janeiro/RJ, Phone: (21) 8204-7102.
1
Bibliography: Effectiveness of Image Quality Assessment Indexes on
Detection of Structural and Nonstructural Distortions
Michel Alves dos Santos
January, 2014
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1

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Effectiveness of Image Quality Assessment Indexes

  • 1. Effectiveness of Image Quality Assessment Indexes on Detection of Structural and Nonstructural Distortions Michel Alves dos Santos ∗ January, 2014 Abstract The main objective of this theme is to provide a study of effectiveness of the main image quality indexes in relation to the detection of distor- tions introduced after processes of acquisition, compression, filtering or sampling, as well as introducing a range of "admissibility" of distortions and degradation classes (like classes of noise, classes of blocking, classes of compression, classes of fusion/blending, classes of watermarking, etc.). Keywords: image processing, contrast distortion, image distortion, im- age processing applications, loss of correlation, luminance distortion, im- age quality index, distortion measurement, dynamic range, image quality, mathematical models, human visual system, signal to noise ratio ∗Michel Alves dos Santos - Alves, M. - malves@cos.ufrj.br - http://www.michelalves.com. MSc. Candidate in Computer Graphics, Image Processing and Computer Vision. http://www.lcg.ufrj.br - Laboratory of Computer Graphics - LCG. Graduate Program in Systems Engineering and Computing (PESC). Alberto Luiz Coimbra In- stitute for Graduate Studies and Research in Engineering - COPPE. Federal University of Rio de Janeiro (UFRJ - http://www.ufrj.br), Brazil - Rio de Janeiro/RJ, Phone: (21) 8204-7102. 1
  • 2. Bibliography: Effectiveness of Image Quality Assessment Indexes on Detection of Structural and Nonstructural Distortions Michel Alves dos Santos January, 2014 References Freeman, J. (2012), Computation and representation in the primate visual system, PhD thesis, Center for Neural Sci- ence, New York University, New York, NY. Guerrero-Colón, J. A., Simoncelli, E. P. & Portilla, J. (2008), Image denoising using mixtures of Gaussian scale mixtu- res, in ‘Proc 15th IEEE Int’l Conf on Image Proc’, IEEE Computer Society, San Diego, CA, pp. 565–568. Lyu, S. & Simoncelli, E. P. (2009), Reducing statistical de- pendencies in natural signals using radial Gaussianiza- tion, in D. Koller, D. Schuurmans, Y. Bengio & L. Bot- tou, eds, ‘Adv. Neural Information Processing Systems (NIPS*08)’, Vol. 21, MIT Press, Cambridge, MA, pp. 1009– 1016. Moorthy, A. K. & Bovik, A. C. (2011), ‘Visual quality assess- ment algorithms: What does the future hold?’, Multimedia Tools Appl. 51(2), 675–696. Rajashekar, U. & Simoncelli, E. P. (2009), Multiscale de- noising of photographic images, in A. C. Bovik, ed., ‘The Essential Guide to Image Processing’, 2nd ed., Academic Press, chapter 11, pp. 241–261. Rajashekar, U., Wang, Z. & Simoncelli, E. P. (2009), Quan- tifying color image distortions based on adaptive spatio- chromatic signal decompositions, in ‘Proc 16th IEEE Int’l Conf on Image Proc’, IEEE Computer Society, Cairo, Egypt, pp. 2213–2216. Rajashekar, U., Wang, Z. & Simoncelli, E. P. (2010), Per- ceptual quality assessment of color images using adap- tive signal representation, in B. Rogowitz & T. N. Pappas, eds, ‘Proc SPIE on Human Vision and Electronic Imaging, XV’, Vol. 7527, Society of Photo-Optical Instrumentation, San Jose, CA. Simoncelli, E. P. (2005), Statistical modeling of photographic images, in A. Bovik, ed., ‘Handbook of Image and Video Processing’, Academic Press, chapter 4.7, pp. 431–441. 2nd edition. Simoncelli, E. P. (2009), Capturing visual image properties with probabilistic models, in A. C. Bovik, ed., ‘The Essen- tial Guide to Image Processing’, 2nd ed., Academic Press, chapter 9, pp. 205–223. Wang, Z. & Simoncelli, E. P. (2004), Stimulus synthesis for efficient evaluation and refinement of perceptual image quality metrics, in B. Rogowitz & T. N. Pappas, eds, ‘Proc. SPIE, Conf on Human Vision and Electronic Imaging IX’, Vol. 5292, San Jose, CA, pp. 99–108. Wang, Z. & Simoncelli, E. P. (2005a), Reduced reference image quality assessment using a wavelet domain natural image statistic model, in B. Rogowitz, T. N. Pappas & S. J. Daly, eds, ‘Proc. SPIE, Conf. on Human Vision and Electronic Imaging X’, Vol. 5666, San Jose, CA, pp. 149– 159. Wang, Z. & Simoncelli, E. P. (2005b), Translation insensitive image similarity in the complex wavelet domain, in ‘Proc. Int’l Conf Acoustics Speech Signal Processing (ICASSP)’, Vol. II, IEEE Sig Proc Society, Philadelphia, PA, pp. 573– 576. Wang, Z., Bovik, A. & Lu, L. (2002), Why is image quality assessment so difficult?, in ‘Acoustics, Speech, and Sig- nal Processing (ICASSP), 2002 IEEE International Con- ference on’, Vol. 4, pp. IV–3313–IV–3316. Wang, Z., Bovik, A. C. & Simoncelli, E. P. (2005), Structu- ral approaches to image quality assessment, in A. Bovik, ed., ‘Handbook of Image and Video Processing’, Academic Press, chapter 8.3, pp. 961–974. 2nd edition. Wang, Z., Bovik, A. C., Sheikh, H. R. & Simoncelli, E. P. (2004), ‘Perceptual image quality assessment: From error visibility to structural similarity’, IEEE Trans Image Pro- cessing 13(4), 600–612. Recipient, IEEE Signal Processing Society Best Paper Award, 2009. Wang, Z., Simoncelli, E. P. & Bovik, A. C. (2003), Multis- cale structural similarity for image quality assessment, in ‘Proc 37th Asilomar Conf on Signals, Systems and Com- puters’, Vol. 2, IEEE Computer Society, Pacific Grove, CA, pp. 1398–1402. Wang, Z., Wu, G., Sheikh, H. R., Simoncelli, E. P., Yang, E. & Bovik, A. C. (2006), ‘Quality-aware images’, IEEE Trans Image Processing 15(6), 1680–1689. Yu, H. & Liu, X. (2011), Structure similarity image quality assessment based on visual perception., in ‘EMEIT’, IEEE, pp. 1519–1522. Zhang, F. & Xu, Y. (2009), Image quality evaluation ba- sed on human visual perception, in ‘Proceedings of the 21st Annual International Conference on Chinese Control and Decision Conference’, CCDC’09, IEEE Press, Pisca- taway, NJ, USA, pp. 1542–1545. URL http://dl.acm. org/citation.cfm?id=1714472.1714772. Zhang, L., , L. Z., Mou, X. & Zhang, D. (2011), ‘Fsim: A feature similarity index for image quality assessment.’, IEEE Transactions on Image Processing 20(8), 2378– 2386. URL http://dblp.uni-trier.de/db/journals/ tip/tip20.html#ZhangZMZ11. 1