Abstract
In this chapter we first describe some HVS-based approaches which try to model the visual processing stream described above, since these approaches were originally used to predict visual quality. We then describe recently proposed structural and information-theoretic approaches and feature-based approaches which are commonly used. Further, we describe recent motion-modeling based approaches, and detail performance evaluation and validation techniques for VQA algorithms. Finally, we touch upon some possible future directions for research on VQA and conclude the chapter.
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Notes
- 1.
The receptive field of a neuron is its response to visual stimuli, which may depend on spatial frequency, movement, disparity or other properties. As used here, the receptive field response may be viewed as synonymous with the signal processing term impulse response.
References
Z. Wang and A. C. Bovik, Modern Image Quality Assessment. New York: Morgan and Claypool Publishing Co., 2006.
A. K. Moorthy and A. C. Bovik, “Perceptually Significant Spatial Pooling techniques for Image quality assessment ,” in SPIE Conference on Human Vision and Electronic Imaging, Jan. 2009.
“Methodology for the subjective assessment of the quality of television pictures,” ITU-R Recommendation BT.500–11.
B. Hiremath, Q. Li and Z. Wang “Quality-aware video,”IEEE International Conference on Image Processing, San Antonio, TX, Sept. 16–19, 2007.
H. R. Sheikh, A. C. Bovik, and L. Cormack, “No-reference quality assessment using natural scene statistics: JPEG2000,”Image Processing, IEEE Transactions on, vol. 14, no. 11, pp. 1918–1927, 2005.
C. M. Liu, J. Y. Lin, K. G. Wu and C. N. Wang, “Objective image quality measure for block-based DCT coding,” IEEE Trans. Consum. Electron., vol. 43, pp. 511–516, 1997.
Z. Wang, A. C. Bovik, and B. L. Evans, “Blind measurement of blocking artifacts in images,” in IEEE Intl. Conf. Image Proc, 2000.
X. Li, “Blind image quality assessment”, IEEE International Conference on Image Processing, New York, 2002.
Patrick Le Callet, Christian Viard-Gaudin, Stéphane Péchard and Emilie Caillault, “No reference and reduced reference video quality metrics for end to end QoS monitoring”, Special Issue on multimedia Qos evaluation and management technologies, E89, (2), Pages: 289–296, February 2006.
W. S. Geisler and M. S. Banks, “Visual performance,” in Handbook of Optics, M. Bass, Ed. McGraw-Hill, 1995.
B. A. Wandell, Foundations of Vision. Sunderland, MA: Sinauer Associates Inc., 1995.
N. C. Rust, V Mante, E. P. Simoncelli, and J. A. Movshon, “How MT cells analyze the motion of visual patterns ”, Nature Neuroscience, vol.9(11), pp. 1421–1431, Nov 2006.
Z. Wang, G. Wu, H. R. Sheikh, E. P. Simoncelli, E.-H. Yang and A. C. Bovik, ”Quality -aware images” IEEE Transactions on Image Processing, vol. 15, no. 6, pp. 1680–1689, June 2006.
R. T. Born and D. C. Bradley, “Structure and function of visual area MT,” Annual Rev Neuroscience, vol. 28, pp. 157–189, 2005.
M. A. Smith, N. J. Majaj, and J. A. Movshon, “Dynamics of motion signaling by neurons in macaque area MT,” Nature Neuroscience, vol. 8, no. 2, pp. 220–228, Feb. 2005.
S. Daly, “The visible differences predictor: an algorithm for the assessment of image fidelity,” in Digital Images and Human Vision (A. B. Watson, ed.), pp. 179–206, Cambridge, MA: The MIT Press, 1993.
J. Lubin, “The use of psychophysical data and models in the analysis of display system performance,” in Digital Images and Human Vision (A. B. Watson, ed.), pp. 163–178, Cambridge, MA: The MIT Press, 1993.
R. J. Safranek and J. D. Johnston, “A perceptually tuned sub-band image coder with image dependent quantization and post-quantization data compression,” in Proc. ICASSP-89, vol. 3, (Glasgow, Scotland), pp. 1945–1948, May 1989.
A. B.Watson, “DCTune: a technique for visual optimization of dct quantization matrices for individual images,” Society for Information Display Digest of Technical Papers, vol. 24, pp. 946–949, 1993.
K. Seshadrinathan, R. J. Safranek, J. Chen, T. N. Pappas, H. R. Sheikh, E. P. Simoncelli, Z. Wang and A. C. Bovik. Image quality assessment. In A. C. Bovik, editor, The Essential Guide to Image Processing, chapter 20. Academic Press, 2009.
C. J. van den Branden Lambrecht and O. Verscheure, “Perceptual quality measure using a spatiotemporal model of the human visual system,” in Proc. SPIE, vol. 2668, no. 1. San Jose, CA, USA: SPIE, Mar. 1996, pp. 450–461.
S. Winkler, “Perceptual distortion metric for digital color video,” Proc. SPIE, vol. 3644, no. 1, pp. 175–184, May 1999.
E. P. Simoncelli, W. T. Freeman, E. H. Adelson, and D. J. Heeger, “Shiftable multiscale transforms,” IEEE Trans. Inform. Theory, vol. 38, pp. 587–607, Mar. 1992.
A. B. Watson, J. Hu, and J. F. McGowan III, “Digital video quality metric based on human vision,” J. Electron. Imaging, vol. 10, no. 1, pp. 20–29, Jan. 2001.
M. Masry, S. S. Hemami, and Y. Sermadevi, “A scalable wavelet-based video distortion metric and applications,”Circuits and Systems for Video Technology, IEEE Transactions on, vol. 16, no. 2, pp. 260–273, 2006.
H. Peterson, A.J. Ahumada, Jr. and A. Watson, ”An Improved Detection Model for DCT Coefficient Quantization,” Human Vision and Electronic Imaging, Proc. SPIE, 1913, 191–201
M. Carnec, P. Le Callet, and D. Barba, “Objective quality assessment of color images based on a generic perceptual reduced reference,” Signal Processing: Image Communication, Volume 23 , Issue 4, Pages 239–256, April 2008.
K. Seshadrinathan and A. C. Bovik. Video quality assessment. In A. C. Bovik, editor, The Essential Guide to Video Processing, chapter 14. Academic Press, 2009.
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, vol. 13, no. 4, pp. 600–612, 2004.
Z. Wang and A. C. Bovik, “A universal image quality index,” IEEE Signal Processing Letters, vol. 9, no. 3, pp. 81–84, 2002.
Z. Wang, E. P. Simoncelli, and A. C. Bovik, “Multiscale structural similarity for image quality assessment,” in Thirty-Seventh Asilomar Conf. on Signals, Systems and Computers, Pacific Grove, CA, 2003.
Z. Wang and E. P. Simoncelli, “Translation insensitive image similarity in complex wavelet domain,” in IEEE Intl. Conf. Acoustics, Speech, and Signal Process., Philadelphia, PA, 2005.
M. P. Sampat, Z. Wang, S. Gupta, A. C. Bovik and M. K. Markey, ”Complex wavelet structural similarity: A new image similarity index,” IEEE Transactions on Image Processing, to appear 2009.
Z. Wang and X. Shang, “Spatial pooling strategies for perceptual image quality assessment,” in IEEE International Conference on Image Processing, Jan. 1996
A. K. Moorthy and A. C. Bovik, “Visual importance pooling for image quality assessment,” IEEE Journal of Selected Topics in Signal Processing, Special Issue on Visual Media Quality Assessment, to appear, April 2009.
Z. Wang, L. Lu, and A. C. Bovik, “Video quality assessment based on structural distortion measurement,” Signal Processing: Image Communication, vol. 19, no. 2, pp. 121–132, Feb. 2004.
A. Srivastava, A. B. Lee, E. P. Simoncelli, and S.-C. Zhu, “On advances in statistical modeling of natural images,” J. Math. Imag. Vis., vol. 18, pp. 17–33, 2003.
E. P. Simoncelli and B. A. Olshausen, “Natural image statistics and neural representation,” Annu. Rev. Neurosci., vol. 24, pp. 1193–1216, May 2001.
H. R. Sheikh and A. C. Bovik, “A visual information fidelity approach to video quality assessment,” First International Workshop on Video Processing and Quality Metrics for Conusmer Electronics, Jan. 2005.
H. R. Sheikh and A. C. Bovik, “Image information and visual quality,” IEEE Trans. Image Process, vol. 15, no. 2, pp. 430–444, 2006.
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., vol. 14, no. 12, pp. 2117–2128, 2005.
J. Malo, I. Epifanio, R. Navarro, and E. P. Simoncelli, “Non-linear image representation for efficient perceptual coding”, IEEE Transactions on Image Processing, vol.15(1), pp. 68–80, Jan 2006.
J. Portilla and E. P. Simoncelli, “ A parametric texture model based on joint statistics of complex wavelet coefficients”, International Journal of Computer Vision, vol.40(1), pp. 49–71, Dec 2000.
J. A. Guerrero-Colón, E. P. Simoncelli , and J. Portilla, “Image denoising using mixtures of Gaussian scale mixtures “, IEEE International Conference on Image Processing, pp. 565–568, Oct 2008.
M. J. Wainwright, E. P. Simoncelli, and A. S. Wilsky, “Random cascades on wavelet trees and their use in analyzing and modeling natural images,” Applied and Computational Harmonic Analysis, vol. 11, pp. 89–123, 2001.
M. J. Wainwright and E. P. Simoncelli, “Scale Mixtures of Gaussians and the statistics of natural images”, Adv. Neural Information Processing Systems (NIPS’99), vol.12 pp. 855–861, May 2000.
A. P. Hekstra, J. G. Beerends, D. Ledermann, F. E. de Caluwe, S. Kohler, R. H. Koenen, S. Rihs, M. Ehrsam, and D. Schlauss, “PVQM - A perceptual video quality measure,” Signal Proc.: Image Comm. vol. 17, pp. 781–798, 2002.
Opticom. [Online]. Available: http://www.opticom.de/technology/pevq-video-quality-testing.html
M. Malkowski and D. Claben, “Performance of video telephony services in UMTS using live measurements and network emulation,” Wireless Personal Comm., vol. 1, pp. 19–32, 2008.
M. Barkowsky, J. Bialkowski, R. Bitto, and A. Kaup, “Temporal registration using 3D phase correlation and a maximum likelihood approach in the perceptual evaluation of video quality,” in IEEE Workshop on Multimedia Signal Proc., 2007.
The Video Quality Experts Group. (2000) Final report from the video quality experts group on the validation of objective quality metrics for video quality assessment. [Online]. Available: http://www.its.bldrdoc.gov/vqeg/projects/frtv phaseI
Objective perceptual multimedia video quality measurement in the presence of a full reference, International Telecommunications Union Std. ITU-T Rec. J. 247, 2008.
M. H. Pinson and S. Wolf, “A new standardized method for objectively measuring video quality,” IEEE Trans. Broadcast., vol. 50, no. 3, pp. 312–322, Sep. 2004.
The Video Quality Experts Group. (2003) Final VQEG report on the validation of objective models of video quality assessment. [Online]. Available: http://www.ts.bldrdoc.gov/vqeg/projects/frtv phaseII
Objective perceptual video quality measurement techniques for digital cable television in the presence of a full reference, International Telecommunications Union Std. ITU-T Rec. J. 144, 2004.
“Video quality metric.” [Online]. Available: http://www.its.bldrdoc.gov/n3/video/VQM_ software.php
M. Yuen and H. R. Wu, “A survey of hybrid MC/DPCM/DCT video coding distortions,” Signal Processing, vol. 70, no. 3, pp. 247–278, Nov. 1998.
J. A. Movshon and W. T. Newsome, “Visual response properties of striate cortical neurons projecting to Area MT in macaque monkeys,” J. Neurosci., vol. 16, no. 23, pp. 7733–7741, 1996.
Z.Wang and Q. Li, “Video quality assessment using a statistical model of human visual speed perception.” J Opt Soc Am A Opt Image Sci Vis, vol. 24, no. 12, pp. B61–B69, Dec 2007.
A. A. Stocker and E. P. Simoncelli, “Noise characteristics and prior expectations in human visual speed perception,” Nature Neuroscience, 9, 578–585 (2006).
Black, M. J. and Anandan, P., “The robust estimation of multiple motions: Parametric and piecewise-smooth flow fields,” Computer Vision and Image Understanding, 63, 75–104 (1996).
K. Seshadrinathan and A. C. Bovik, “Spatio-temporal quality assessment of natural videos,” IEEE Transactions on Image Processing, submitted for publication.
K. Seshadrinathan and A. C. Bovik, “A structural similarity metric for video based on motion models,” IEEE International Conference on Acoustics, Speech, and Signal Processing, 2007.
D. J. Fleet and A. D. Jepson, “Computation of component image velocity from local phase information,” International Journal of Computer Vision, vol. 5, no. 1, pp. 77–104, 1990.
D. J. Heeger, “Optical flow using spatiotemporal filters,” International Journal of Computer Vision, vol. 1, no. 4, pp. 279–302, 1987.
E. H. Adelson and J. R. Bergen, “Spatiotemporal energy models for the perception of motion.” J Opt Soc Am A, vol. 2, no. 2, pp. 284–299, Feb 1985.
N. J. Priebe, S. G. Lisberger, and J. A. Movshon, “Tuning for spatiotemporal frequency and speed in directionally selective neurons of macaque striate cortex.” J Neurosci, vol. 26, no. 11, pp. 2941–2950, Mar 2006.
E. P. Simoncelli and D. J. Heeger, “A model of neuronal responses in visual area MT,” Vision Res, vol. 38, no. 5, pp. 743–761, Mar 1998.
J. G. Daugman, “Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters,” Journal of the Optical Society of America A (Optics and Image Science), vol. 2, no. 7, pp. 1160–1169, 1985.
P. C. Teo and D. J. Heeger, “Perceptual image distortion,” in Proceedings of the IEEE International Conference on Image Processing. IEEE, 1994, pp. 982–986 vol.2.
K. Seshadrinathan and A. C. Bovik, “Unifying analysis of full reference image quality assessment,” in IEEE Intl. Conf. on Image Proc., 2008.
A. B. Watson and J. Ahumada, A. J., “Model of human visual-motion sensing,” Journal of the Optical Society of America A (Optics and Image Science), vol. 2, no. 2, pp. 322–342, 1985.
H. Frank and S. C. Althoen, “The coefficient of variation,” in Statistics: Concepts and Applications. Cambridge, Great Britan: Cambridge University Press., 1995, pp. 58–59.
K. Seshadrinathan, “Video quality assessment based on motion models,” Ph.D. dissertation, University of Texas at Austin, 2008.
H. R. Sheikh, M. F. Sabir, and A. C. Bovik, “A statistical evaluation of recent full reference image quality assessment algorithms,” IEEE Transactions on Image Processing, vol. 15, no. 11, pp. 3440–3451, Nov. 2006.
LIVE image quality assessment database. [Online]. Available: http://live.ece.utexas.edu/research/quality/subjective.html
Wang, Z. and Bovik, A. C., “Mean squared error: Love it or leave it? - a new look at fidelity measures.” IEEE Signal Processing Magazine. January 2009.
“Video coding for low bit rate communication”, ITU Recommendation H.263.
“Generic coding of moving pictures and associated audio information - part 2: Video,” 1994, ITU-T and ISO/IEC JTC 1. ITU-T Recommendation H.262 and ISO/IEC 13 818–2 (MPEG-2).
“Advanced video coding,” 2003, ISO/IEC 14496–10 and ITU-T Rec. H.264.
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Moorthy, A.K., Seshadrinathan, K., Bovik, A.C. (2009). Digital Video Quality Assessment Algorithms. In: Furht, B. (eds) Handbook of Multimedia for Digital Entertainment and Arts. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-89024-1_6
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