Natural Image Statistics
Images, videos, 3D pictures and depth maps of the natural world obey remarkable statistical properties with great regularity. These properties are not overt, but rather are revealed when perceptually relevant processes, such as appropriate bandpass filtering, and nonlinear statistical normalization, are applied to the visual data. LIVE has been quite active in the development of, furtherance of, and application of natural scene models, particularly in regard to image quality. The latter topic turns out to be an excellent realm for studying regular picture statistics, since these statistical regularities can be lost or modified by the presence of visible distortions. There are deep reasons for this, related to the evolution and adaptation of the human eyes to a world of visual signals that obey these statistics, which deeply affect the way visual neurons respond to visual signal of any type. The vision system appears to be remarkably senstitive to perturbations of these statistics, and hence, to visual distortions. LIVE has also conducted a significant amount of research on the natural statistics of images at the point of gaze, and to predict visual saliency. Some of the key papers follow:
R. Raj, W.S. Geisler, R.A. Frazor and A.C. Bovik, “Contrast statistics for foveated vision systems: fixation selection by minimizing contrast entropy,” Journal of the Optical Society of America, vol. 22, no. 10, pp. 2039-2049, October 2005.
H.R. Sheikh, A.C. Bovik and L.K. Cormack, “No-reference quality assessment using natural scene statistics: JPEG2000,” IEEE Transactions on Image Processing, vol. 14, no. 11, pp. 1918-1927, November 2005.
H.R. Sheikh, A.C. Bovik and G. DeVeciana, “An information fidelity criterion for image quality assessment using natural scene statistics,” IEEE Transactions on Image Processing, vol. 14, no. 12, pp. 2117-2128, December 2005.
H.R. Sheikh and A.C. Bovik, “Image information and visual quality,” IEEE Transactions on Image Processing, vol. 15, no. 2, pp. 430-444, February 2006. Recipient of a 2017 Google Scholar Classic Paper citation (for Computer Vision and Pattern Recognition). Google Scholar Classic Papers are highly-cited papers that have stood the test of time, and are among the ten most-cited articles in their area of research published ten years earlier.
U. Rajashekar, L.K. Cormack and A.C. Bovik, “Visual search in noise: Revealing the influence of structural cues by gaze-contingent classification image analysis,” Journal of Vision, Special Issue on Finding Visual Features: Using Stochastic Stimuli, vol. 6, no. 4, art. 7, pp. 379-386, April 2006.
Z. Wang, G. Wu, H.R. Sheikh, E. Simoncelli, E. Yang and A.C. Bovik, “Quality-aware images,” IEEE Transactions on Image Processing, vol. 15, no. 5, pp. 1680-1689, May 2006.
H.R. Sheikh, M.F. Sabir and A.C. Bovik, “An evaluation of recent full reference image quality assessment algorithms,” IEEE Transactions on Image Processing, vol. 15, no. 11, pp. 3440-3451, November 2006. Recipient of a 2017 Google Scholar Classic Paper citation (for Signal Processing). Google Scholar Classic Papers are highly-cited papers that have stood the test of time, and are among the ten most-cited articles in their area of research published ten years earlier.
A. Tavassoli, I. van der Linde, L.K. Cormack and A.C. Bovik, “An efficient technique for exposing visual search strategies with classification images,” Journal of Perception & Psychophysics, vol. 69, no. 1, pp. 103-113, 2007.
R. Raj and A.C. Bovik, “MICA: A multilinear ICA decomposition for natural scene modeling,” IEEE Transactions on Image Processing, vol. 17, no. 3, pp. 259-271, March 2008.
U. Rajashekar, I. van der Linde, A.C. Bovik and L.K. Cormack, “GAFFE: A gaze-attentive fixation finding engine,” IEEE Transactions on Image Processing, vol. 17, no. 4, pp. 564- 573, April 2008.
Y. Liu, L.K. Cormack and A.C. Bovik, “Disparity statistics in natural scenes,” Journal of Vision, vol. 8, no. 11, art. 19, pp. 1-14, August 2008.
Z. Wang and A.C. Bovik, “Mean squared error: Love it or leave it? ― A new look at signal fidelity measures,” IEEE Signal Processing Magazine, vol. 26, no. 1, pp. 98-117, January 2009 (Winner of the IEEE Signal Processing Magazine Best Paper Award for 2013).
I. van der Linde, U. Rajashekar, A.C. Bovik and L.K. Cormack, “DOVES: A database of visual eye movements,” Spatial Vision, vol. 22, no. 2, pp. 161-177, February 2009.
A.K. Moorthy and A.C. Bovik, “Visual importance pooling for image quality assessment,” IEEE Journal on Selected Topics in Signal Processing, Special Issue on Visual Media Quality Assessment, vol. 3, no. 2, pp. 193-201, April 2009.
A.K. Moorthy and A.C. Bovik, “Statistics of natural image distortions,” IEEE International Conference on Acoustics, Speech and Signal Processing, Dallas, Texas, March 14-19, 2010.
A.K. Moorthy and A.C. Bovik, “A two-step framework for constructing blind image quality indices,” IEEE Signal Processing Letters, vol. 17, no. 5, pp. 513-516, May 2010.
K. Seshadrinathan, R. Soundararajan, A.C. Bovik and L.K. Cormack, “Study of subjective and objective quality assessment of video,” IEEE Transactions on Image Processing, vol. 19, no. 6, pp. 1427-1441, June 2010.
M.A. Saad, A.C. Bovik and C. Charrier, “A DCT statistics based blind image quality index,” IEEE Signal Processing Letters, vol. 17, no. 6, pp. 583-586, June 2010.
Y. Liu, L.K. Cormack and A.C. Bovik, “Dichotomy between luminance and disparity features at binocular fixations,” Journal of Vision, vol. 10, no. 12:23, pp. 1-17, December 2010.
K. Seshadrinathan and A.C. Bovik, “Automatic prediction of perceptual quality of multimedia signals – A survey,” International Journal of Multimedia Tools and Applications, Special Issue on Survey Papers in Multimedia by World Experts, vol. 51, no. 1, pp. 163-186, January 2011.
A.K. Moorthy and A.C. Bovik “Visual Quality Assessment Algorithms: What Does the Future Hold?” International Journal of Multimedia Tools and Applications, Special Issue on Survey Papers in Multimedia by World Experts, vol. 51, no. 2, pp. 675-696, February 2011.
C. Li and A.C. Bovik, “Blind image quality assessment using a general regression neural network,” IEEE Transactions on Neural Networks, vol. 22, no. 5, pp. 793-799, May 2011.
M.-J. Chen and A.C. Bovik, “No-reference image blur assessment using multiscale gradient,” EURASIP Journal on Image and Video Processing, Special Issue on Quality of Multimedia Experience, 2011:3 doi:10.1186/1687-5281- 2011-3 [open access], July 2011.
Y. Liu, A.C. Bovik and L.K. Cormack, “Statistical modeling of 3D natural scenes with application to Bayesian stereopsis,” IEEE Transactions on Image Processing, vol. 20, no. 9, pp. 2515-2530, September 2011.
Z. Wang and A.C. Bovik, “Reduced- and no-reference visual quality assessment - The natural scene statistic model approach,” IEEE Signal Processing Magazine, Special Issue on Multimedia Quality Assessment, vol. 29, no. 6, pp. 29-40, November 2011.
A.K. Moorthy and A.C. Bovik, “Blind image quality assessment: From natural scene statistics to perceptual quality,” IEEE Transactions on Image Processing, vol. 20, no. 12, pp. 3350-3364, December 2011.
A. Mittal, G.S. Muralidhar, J. Ghosh and A.C. Bovik, “Blind image quality assessment without human training using latent quality factors,” IEEE Signal Processing Letters, vol. 19, no. 2, pp. 75-78, February 2012.
R. Soundararajan and A.C. Bovik, “RRED indices: Reduced reference entropic differencing for image quality assessment,” IEEE Transactions on Image Processing, vol. 21, no. 2, pp. 517-526, February 2012.
M.A. Saad and A.C. Bovik, “Blind image quality assessment: A natural scene statistics approach in the DCT domain,” IEEE Transactions on Image Processing, vol. 21, no. 8, pp. 3339-3352, August 2012.
A. Mittal, A.K. Moorthy and A.C. Bovik, “No-reference image quality assessment in the spatial domain,” IEEE Transactions on Image Processing, vol. 21, no. 12, pp. 4695-4708, December 2012.
A. Mittal, R. Soundararajan and A.C. Bovik, “Making a ‘completely blind’ image quality analyzer,” IEEE Signal Processing Letters, vol. 21, no. 3, pp. 209-212, March 2013.
R. Soundararajan and A.C. Bovik, “Video quality assessment by reduced reference spatio- temporal entropic differencing,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 23, no. 4, pp. 684-694, April 2013 (Winner of the IEEE Circuits and Systems for Video Technology Best Paper Award for 2016).
C. Li, Y. Ju, A.C. Bovik and X. Wu, “A no-training no reference image quality index using perceptual features,” Optical Engineering, vol. 52, no. 5, 057003, May 2013.
R. Soundararajan and A.C. Bovik, “Survey of information theory and visual quality assessment,” Signal, Image, and Video Processing, Special Section on Human Vision and Information Theory, Invited Paper, vol. 7, no. 3, pp. 391-401, May 2013.
C.-C. Su, A.C. Bovik and L.K. Cormack, “Color and depth priors in natural images,” IEEE Transactions on Image Processing, vol. 22, no. 6, pp. 2259-2274, June 2013.
M.-J. Chen, L.K. Cormack and A.C. Bovik, “No-reference quality assessment of natural stereopairs,” IEEE Transactions on Image Processing, vol. 22, no. 9, pp. 3379-3391, September 2013.
A.C. Bovik, “Automatic prediction of perceptual image and video quality,” Proceedings of the IEEE, Invited Paper, vol. 101, no. 9, doi: 10.1109/JPROC.2013.2257632, pp. 2008-2024, September 2013.
A.K. Moorthy, C.-C. Su, A. Mittal and A.C. Bovik, “Subjective evaluation of stereoscopic image quality,” Signal Processing: Image Communication, vol. 28, no. 9, pp. 870-883, September 2013.
M.-J. Chen, C.-C. Su, D.-K. Kwon, L.K. Cormack and A.C. Bovik, “Full-reference quality assessment of stereopairs accounting for rivalry,” Signal Processing: Image Communication, vol. 28, no. 10, pp. 1143-1155, October 2013.
A.K. Moorthy, A. Mittal and A.C. Bovik, “Perceptually-optimized blind repair of natural images,” Signal Processing: Image Communication, vol. 28, no. 10, pp. 1478-1493, November 2013.
M.-J. Chen, A.C. Bovik and L.K. Cormack, “Study of distortion conspicuity on stereoscopically viewed 3D images,” Journal of the Society for Information Display, vol. 21 no. 11, pp. 491-503, November 2013.
K. Lee, A.K. Moorthy, S. Lee and A.C. Bovik, “3D visual activity assessment based on natural scene statistics,” IEEE Transactions on Image Processing, vol. 23, no. 1, pp. 450- 465, January 2014.
W. Xue, L. Zhang, X. Mou and A.C. Bovik, “Gradient magnitude similarity deviation: A highly efficient perceptual image quality index,” IEEE Transactions on Image Processing, vol. 23, no. 2, pp. 684-695, February 2014.
M. Saad and A.C. Bovik, “Blind prediction of natural video quality,” IEEE Transactions on Image Processing, vol. 23, no. 3, pp. 1352-1365, March 2014.
H. Kim, S. Lee and A.C. Bovik, “Saliency measurement on stereoscopic videos,” IEEE Transactions on Image Processing, vol. 23, no. 4, pp. 1476-1490, April 2014.
L. Liu, H. Dong, H. Huang and A.C. Bovik, “No-reference image quality assessment in the curvelet domain,” Signal Processing: Image Communication, vol. 29, no. 4, pp. 494-505, April 2014.
C. Chen, L.K. Choi, G. de Veciana, C. Caramanis, R.W. Heath, Jr. and A.C. Bovik, “A model of the time-varying subjective quality of HTTP video streams with rate adaptations,” IEEE Transactions on Image Processing, vol. 23, no. 5, pp. 2206-2221, May 2014.
Y. Zhang, A.K. Moorthy, D.M. Chandler and A.C. Bovik, “C-DIIVINE: No-reference image quality assessment based on local magnitude and phase statistics of natural scenes,” Signal Processing: Image Communication, vol. 29, no. 4, pp. 725-747, August 2014.
L. Liu, B. Liu, H. Huang and A.C. Bovik, “No-reference image quality assessment based on spatial and spectral entropies,” Signal Processing: Image Communication, vol. 29, no. 8, pp. 856-863, September 2014.
Q.B. Sang, H.X. Qi, X.J. Wu, C.F. Li and A.C. Bovik, “No-reference image blur index using singular value curve,” Visual Communications and Image Representation, vol. 25, no. 7, pp. 1625-1630, October 2014.
W. Xue, X. Mou, L. Zhang, A.C. Bovik, and X. Feng, “Blind image quality prediction using joint statistics of gradient magnitude and laplacian features,” IEEE Transactions on Image Processing, vol. 23, no. 11, pp. 4850-4862, November 2014.
Q.B. Sang, X.J. Wu, C.F. Li and A.C. Bovik, “Blind image quality assessment using a reciprocal singular value curve,” Signal Processing: Image Communication, vol. 29, no. 10, pp. 1149-1157, November 2014.
T. Eerola, L. Lensu, H. Kalviainen and A.C. Bovik, “Study of no-reference image quality assessment algorithms on printed images,” Journal of Electronic Imaging, Special Section on Image Quality and System Performance, vol. 23, no. 6, pp. 061106-1- 061106-12, November/December 2014.
S. Gunasekar, J. Ghosh and A.C. Bovik, “Face detection on distorted images augmented by perceptual quality-aware features,” IEEE Transactions on Information Forensics and Security, Special Issue on Facial Biometrics in the Wild, vol. 9, no. 12, pp. 2119-2131, December 2014.
C.-C. Su, L.K. Cormack and A.C. Bovik, “Closed form correlation model of oriented bandpass natural images,” IEEE Signal Processing Letters, vol. 21, no. 1, pp. 21-25, January 2015.
C. Chen, X. Zhu, G. de Veciana, A.C. Bovik and R.W. Heath, “Rate adaptation and admission control for video transmission with subjective quality constraints,” IEEE Journal on Selected Topics in Signal Processing, Special Issue on Visual Signal Processing for Wireless Networks (VSPWN), vol. 9, no. 1, pp. 22-36, February 2015.
W. Huang, X. Cao, K. Lu, Q. Dai and A.C. Bovik, “Towards naturalistic 2D-to- 3D conversion,” IEEE Transactions on Image Processing, vol. 24, no. 2, pp. 724-733, February 2015.
J. Park, H. Oh, S. Lee and A.C. Bovik, “3D visual discomfort predictor: Analysis of disparity and neural activity statistics,” IEEE Transactions on Image Processing, vol. 24, no. 3, pp. 1101-1114, March 2015.
C.-C. Su, L.K. Cormack and A.C. Bovik, “Oriented correlation models of distorted natural images with application to natural stereopair quality evaluation,” IEEE Transactions on Image Processing, vol. 24, no. 5, pp. 1685-1699, May 2015.
L. Zhang, L. Zhang and A.C. Bovik, “A feature-enriched completely blind local image quality analyzer,” IEEE Transactions on Image Processing, vol. 24, no. 8, pp. 2579-2591, August 2015.
L.K. Choi, J. You and A.C. Bovik, “Referenceless prediction of perceptual fog density and perceptual image defogging,” IEEE Transactions on Image Processing, vol. 24, no. 11, pp. 3888-3901, November 2015.
L. Liu, Y. Hua, Q. Zhao, H. Huang and A.C. Bovik, “Blind image quality assessment by relative gradient statistics and Adaboosting neural network,” Signal Processing: Image Communication, vol. 40, no. 1, pp. 1-15, January 2016.
T.R. Goodall, A.C. Bovik and N.G. Paulter, Jr., “Tasking on natural statistics of infrared images,” IEEE Transactions on Image Processing, vol. 25, no. 1, pp. 65-79, January 2016.
A. Mittal, M. Saad and A.C. Bovik, “A ‘completely blind’ video integrity oracle,” IEEE Transactions on Image Processing, vol. 25, no. 1, pp. 289-300, January 2016.
D. Ghadiyaram and A.C. Bovik, “Massive online crowdsourced study of subjective and objective picture quality,” IEEE Transactions on Image Processing, vol. 25, no. 1, pp. 372- 387, January 2016.
F. Xie, Y. Lu, A.C. Bovik, Z. Jiang and R. Meng, “Application-driven no reference quality assessment for dermoscopy images with multiple distortions,” IEEE Transactions on Biomedical Engineering, vol. 63, no. 6, pp. 1248-1256, June 2016.
T. Goodall, A.C. Bovik, Z. Li, A. Aaron and I. Katsavounidis, “Blind picture upscaling ratio prediction,” IEEE Signal Processing Letters, vol. 23, no. 12, pp. 1801-1805, December 2016.
D. Ghadiyaram and A.C. Bovik, “Perceptual quality prediction on authentically distorted images using a bag of features approach,” Journal of Vision, vol. 17, no. 1, art. 32, doi:10.1167/17.1.32, pp. 1-25, January 2017.
C.-C. Su, L.K. Cormack and A.C. Bovik, “Bayesian depth estimation from monocular natural images,” Journal of Vision, vol. 17, no. 5, article 22, pp. 1-29, May 2017.
D. Kundu, D. Ghadiyaram, A.C. Bovik and B.L. Evans, “No-reference quality assessment of tone-mapped HDR pictures,” IEEE Transactions on Image Processing, vol. 26, no. 6, pp. 2957-2971, June 2017.
D.E. Moreno-Villamarin, H.D. Benitez-Restrepo and A.C. Bovik, “Predicting the quality of fused long wave infrared and visible light images,” IEEE Transactions on Image Processing, vol. 26, no. 7, pp. 3479-3491, July 2017.
C. Bampis and A.C. Bovik, “Continuous prediction of streaming video QoE using dynamic networks,” IEEE Signal Processing Letters, vol. 24, no. 7, pp. 1083-1087, July 2017.
B. Yan, B. Bare, K. Li, J. Li and A.C. Bovik, “Learning based quality assessment for image retargeting,” Signal Processing: Image Communication, vol. 56, pp. 12-19, August 2017.
K. Gu, J. Zhou, G. Zhai, W. Lin and A.C. Bovik, “No-reference quality assessment of screen content pictures,” IEEE Transactions on Image Processing, vol. 26, no. 8, pp. 4005-4017, August 2017.
D. Kundu, D. Ghadiyaram, A.C. Bovik and B.L. Evans, “Large-scale crowdsourced study for tone-mapped HDR pictures,” IEEE Transactions on Image Processing, vol. 26, no. 10, pp. 4725-4740, October 2017.
J. Kim, T. Kim, S. Lee and A.C. Bovik, “Quality assessment of perceptual crosstalk on two- view auto-stereoscopic displays,” IEEE Transactions on Image Processing, vol. 26, no. 10, pp. 4885-4899, October 2017.
D. Ghadiyaram, J. Pan, A.C. Bovik, A. Moorthy, P. Panda and K.C. Yang, “In-capture mobile video distortions: A study of subjective behavior and objective algorithms,” IEEE Transactions on Circuits and Systems for Video Technology, to appear.
C. Bampis, Z. Li, A.K. Moorthy, I. Katsavounidis, A. Aaron and A.C. Bovik, “Study of temporal effects on subjective video quality of experience,” IEEE Transactions on Image Processing, to appear.
C. Bampis, P. Gupta, R. Soundararajan and A.C. Bovik, “SpEED-QA: Spatial efficient entropic differencing for image and video quality,” IEEE Signal Processing Letters, vol. 24, no. 9, pp. 1333-1337, September 2017.
D. Kundu, D. Ghadiyaram, A.C. Bovik and B.L. Evans, “Large-scale crowdsourced study for tone-mapped HDR pictures,” IEEE Transactions on Image Processing, vol. 26, no. 10, pp. 4725-4740, October 2017.