Perceptual image quality assessment using a normalized Laplacian pyramid

Human Vision and Electronic Imaging 2016, HVEI 2016

Volumn , Issue , 2016, Pages 43-48

  Laparra, Valero ^a,b   Balle, Johannes ^c   Berardino, Alexander ^c   Simoncelii, Eero P ^c  

^a NEW YORK UNIVERSITY   (United States)

^b UNIVERSITY OF VALENCIA   (Spain)

^c NEW YORK UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

GAIN CONTROL; IMAGE QUALITY; LAPLACE TRANSFORMS; LUMINANCE; MEAN SQUARE ERROR;

DISTORTED IMAGES; IMAGE QUALITY METRICS; NORMALIZED LAPLACIAN; ORIGINAL IMAGES; PERCEPTUAL IMAGE QUALITY; PERCEPTUAL JUDGEMENTS; QUALITY METRICES; ROOT MEAN SQUARED ERRORS;

QUALITY CONTROL;

EID: 85011092689     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.2352/ISSN.2470-1173.2016.16HVEI-103     Document Type: Conference Paper

References (33)

1. B. Girod, "Digital images and human vision, " ch. What's Wrong with Mean-squared Error?, pp. 207-220, MIT Press, 1993.
2. P. Teo and D. Heeger, "Perceptual image distortion, " in Image Processing, 1994. Proceedings. ICIP-94., IEEE International Conference, 2, pp. 982-986 vol.2, Nov 1994.
3. Z. Wang and A. Bovik, "Mean squared error: Love it or leave it? a new look at signal fidelity measures, " Signal Processing Magazine 26, pp. 98-117, Jan. 2009.
4. A. M. Eskicioglu and P. S. Fisher, "Image quality measures and their performance, " IEEE Trans. Communications 43, pp. 2959-2965, Dec. 1995.
5. M. P. Eckert and A. P. Bradley, "Perceptual quality metrics applied to still image compression, " Signal Processing 70, pp. 177-200, Nov. 1998.
6. A. B. Watson, "DCTune: A technique for visual optimization of DCT quantization matrices for individual images., " Society for Information Display Digest of Technical Papers, pp. 245-283, 946-949.
7. S. Daly, "Digital images and human vision, " ch. The Visible Differences Predictor: An Algorithm for the Assessment of Image Fidelity, pp. 179-206, MIT Press, Cambridge, MA, USA, 1993.
8. J. Lubin, "A visual discrimination mode for image system design and evaluation, " Visual Models for Target Detection and Recognition, pp. 245-283, 1995.
9. A. B. Watson and J. A. Solomon, "Model of visual contrast gain control and pattern masking, " Journal of Optical Society of America 14(9), pp. 2379-2391, 1997.
10. V. Laparra, J. Munoz Man, and J. Malo, "Divisive normalization image quality metric revisited, " JOSA A 27(4), pp. 852-864, 2010.
11. H. B. Barlow, "Possible principles underlying the transformation of sensory messages, " in Sensory Communication, W. A. Rosenblith, ed., pp. 217-234, MIT Press, Cambridge, MA, 1961.
12. E. P. Simoncelli and B. Olshausen, "Natural image statistics and neural representation, " Annual Review of Neuroscience 24, pp. 1193-1216, May 2001.
13. A. J. Bell and T. J. Sejnowski, "An information-maximization approach to blind separation and blind deconvolution, " Neural Computation 7, pp. 1129-1159, Nov. 1995.
14. B. Olshausen and D. Field, "Emergence of simple-cell receptive field properties by learning a sparse code for natural images, " Nature 381, pp. 607-609, 1996.
15. M. Carandini and D. J. Heeger, "Normalization as a canonical neural computation., " Nature Reviews Neurosci 13, pp. 51-62, Jan. 2012.
16. E. P. Simoncelli, "Statistical models for images: Compression, restoration and synthesis, " in Proc 31st Asilomar Conf on Signals, Systems and Computers, 1, pp. 673-678, IEEE Computer Society, (Pacific Grove. CA), Nov 2-5 1997.
17. O. Schwartz and E. Simoncelli, "Natural signal statistics and sensory gain control, " Nat. Neurosci. 4(8), pp. 819-825, 2001.
18. J. Malo, I. Epifanio, R. Navarro, and E. P. Simoncelli, "Nonlinear image representation for efficient perceptual coding, " IEEE Transactions on Image Processing 15(1), pp. 68-80, 2006.
19. J. Malo and V. Laparra, "Psychophysical tuned divisive normalization approximately factorizes the PDF of natural images, " Neural Computation 22(12), pp. 3179-3206, 2010.
20. S. Lyu, "Dependency Reduction with Divisive Normalization: Justification and Effectiveness, " Neural Computation 23(11), pp. 2942-2973, 2011.
21. Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, "Image quality assessment: From error visibility to structural similarity, " IEEE Trans, lm. Proc. 13, pp. 600-612, Apr 2004.
22. Z. Wang, E. P. Simoncelli, and A. C. Bovik, "Multi-scale structural similarity for image quality assessment, " in in Proc. IEEE Asilomar Conf. on Signals, Systems, and Computers, pp. 1398-1402, 2003.
23. V. Mante, V. Bonin, and M. Carandini, "Functional mechanisms shaping lateral geniculate responses to artificial and natural stimuli, " Neuron 58(4), pp. 625-638, 2008.
24. P. J. Burt, Edward, and E. H. Adelson,'The Laplacian pyramid as a compact image code, " IEEE Transactions on Communications 31, pp. 532-540, 1983.
25. N. Ponomarenko, V. Lilian. A. Zelensky, K. Egiazarian, M. Carli, and F. Battisti, "TID2008-a database for evaluation of full-reference visual quality assessment metrics, " Advances of Modern Radioelectronics 10, pp. 30-45, 2009.
26. A. Olmos and F. A. A. Kingdom, "A biologically inspired algorithm for the recovery of shading and reflectance images, " Perception 33, pp. 1463-1473, 2004.
27. T. M. Cover and J. A. Thomas, Elements of Information Theory, Wiley-Interscience, 2nd ed., July 2006.
28. A. Bell and T. Sejnowski, "The'independent components'of natural scenes are edge filters, " Vision Research 37(23), pp. 3327-3338, 1997.
29. R. Baddeley, "Searching lor filters with'interesting'output distributions: An uninteresting direction to explore, " Network 7, pp. 409-421, 1996.
30. M. Bethge, "Factorial coding of natural images: how effective are linear models in removing higher-order dependencies?, " Journal of the Optical Society of America A 23(6), pp. 1253-1268, 2006.
31. S. Lyu and E. P. Simoncelli, "Nonlinear extraction of'independent components'of natural images using radial Gaussianization, " Neural Computation 21, pp. 1485-1519, Jun 2009.
32. A. Mittal, A. K. Moorthy, and A. C. Bovik, "No-reference image quality assessment in the spatial domain, " IEEE Trans. Image Process, pp. 4695-4708, 2012.
33. Z. Wang and E. P. Simoncelli, "Maximum differentiation (MAD) competition: A methodology for comparing computational models of perceptual quantities, " Journal of Vision 8(12), p. 8, 2008.