Vision models for image quality assessment: one is not enough

A number of image quality metrics are based on psy- chophysical models of the human visual system. We propose a new framework for image quality assessment, gathering three indexes describing the image quality in terms of visual performance, visual appearance, and visual attention. These indexes are built on three vision models grounded on psychophysical data: we use models from Mantiuk et al. (visual performance), Moroney et al. (visual ap- pearance), and Itti, Koch, and Niebur (visual attention). For accuracy reasons, the sensor and display system characteristics are taken into account in the evaluation process, so that these indexes character- ize the image acquisition, processing, and display pipeline. We give evidence that the three image quality indexes, all derived from psy- chophysical data, are very weakly correlated. This emphasizes the need for a multicomponent description of image quality. © 2010 SPIE and IS&T. (DOI: 10.1117/1.3495989)

[1]  Hiroshi Yamaguchi,et al.  Evaluating HDR rendering algorithms , 2007, TAP.

[2]  Jeffrey Lubin,et al.  A VISUAL DISCRIMINATION MODEL FOR IMAGING SYSTEM DESIGN AND EVALUATION , 1995 .

[3]  Christof Koch,et al.  A Model of Saliency-Based Visual Attention for Rapid Scene Analysis , 2009 .

[4]  C. Koch,et al.  A saliency-based search mechanism for overt and covert shifts of visual attention , 2000, Vision Research.

[5]  E. Peli.  Contrast in complex images. , 1990, Journal of the Optical Society of America. A, Optics and image science.

[6]  Masaki Emoto,et al.  Comparing realness between real objects and images at various resolutions , 2007, Electronic Imaging.

[7]  Reiner Lenz,et al.  Evaluation and unification of some methods for estimating reflectance spectra from RGB images. , 2008, Journal of the Optical Society of America. A, Optics, image science, and vision.

[8]  D. Amnon Silverstein,et al.  The relationship between image fidelity and image quality , 1996, Proceedings of 3rd IEEE International Conference on Image Processing.

[9]  A. Treisman,et al.  A feature-integration theory of attention , 1980, Cognitive Psychology.

[10]  Robert W. G. Hunt,et al.  The reproduction of colour , 1957 .

[11]  David W. Jacobs,et al.  Mesh saliency , 2005, SIGGRAPH 2005.

[12]  Andrew B. Watson,et al.  The cortex transform: rapid computation of simulated neural images , 1987 .

[13]  Jacob Nachmias,et al.  On the psychometric function for contrast detection , 1981, Vision Research.

[14]  Roland Brémond,et al.  A tone-mapping operator for road visibility experiments , 2008, TAP.

[15]  Mark D. Fairchild,et al.  iCAM06: A refined image appearance model for HDR image rendering , 2007, J. Vis. Commun. Image Represent..

[16]  Donald P. Greenberg,et al.  An experimental evaluation of computer graphics imagery , 1986, TOGS.

[17]  Nagato Narita,et al.  Method for the Subjective Assessment of the Quality of Television Pictures Recommended by CCIR Rec. 500-5. , 1993 .

[18]  S. S. Stevens,et al.  Brightness function: effects of adaptation. , 1963, Journal of the Optical Society of America.

[19]  Michael H. Brill,et al.  Color appearance models , 1998 .

[20]  J. Robson,et al.  Application of fourier analysis to the visibility of gratings , 1968, The Journal of physiology.

[21]  Andrew S. Glassner,et al.  How to derive a spectrum from an RGB triplet , 1989, IEEE Computer Graphics and Applications.

[22]  Claudio M. Privitera,et al.  Focused JPEG encoding based upon automatic preidentified regions of interest , 1999, Electronic Imaging.

[23]  Ann McNamara,et al.  Visual Perception in Realistic Image Synthesis , 2001, Comput. Graph. Forum.

[24]  C. Koch,et al.  Computational modelling of visual attention , 2001, Nature Reviews Neuroscience.

[25]  Christof Koch,et al.  Modeling attention to salient proto-objects , 2006, Neural Networks.

[26]  Scott J. Daly,et al.  Visible differences predictor: an algorithm for the assessment of image fidelity , 1992, Electronic Imaging.

[27]  Hans-Peter Seidel,et al.  Predicting visible differences in high dynamic range images: model and its calibration , 2005, IS&T/SPIE Electronic Imaging.

[28]  Roy S. Berns,et al.  Colorimetric Characterization of a Computer-Controlled Liquid Crystal Display , 2004 .

[29]  John K. Tsotsos,et al.  Modeling Visual Attention via Selective Tuning , 1995, Artif. Intell..

[30]  Harald Haas,et al.  Asilomar Conference on Signals, Systems, and Computers , 2006 .

[31]  Eero P. Simoncelli,et al.  Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.

[32]  David J. Sakrison,et al.  The effects of a visual fidelity criterion of the encoding of images , 1974, IEEE Trans. Inf. Theory.

[33]  Roberto Scopigno,et al.  Computer Graphics forum , 2003, Computer Graphics Forum.

[34]  Wolfgang Heidrich,et al.  High dynamic range display systems , 2004, SIGGRAPH 2004.

[35]  Michael P. Eckert.  Lossy compression using wavelets, block DCT, and lapped orthogonal transforms optimized with a perceptual model , 1997, Medical Imaging.

[36]  M. Luo,et al.  Uniform colour spaces based on CIECAM02 colour appearance model , 2006 .

[37]  Holly E. Rushmeier,et al.  Tone reproduction for realistic images , 1993, IEEE Computer Graphics and Applications.

[38]  Stefan Winkler,et al.  Digital Video Quality: Vision Models and Metrics , 2005 .

[39]  Mahdi Nezamabadi,et al.  Color Appearance Models , 2014, J. Electronic Imaging.

[40]  John E. Howland,et al.  Computer graphics , 1990, IEEE Potentials.

[41]  J. M. Foley,et al.  Contrast masking in human vision. , 1980, Journal of the Optical Society of America.

[42]  Bruce Walter,et al.  Visual equivalence: towards a new standard for image fidelity , 2007, SIGGRAPH 2007.

[43]  E. Knudsen.  Fundamental components of attention. , 2007, Annual review of neuroscience.

[44]  Laurent Itti,et al.  Realistic avatar eye and head animation using a neurobiological model of visual attention , 2004, SPIE Optics + Photonics.

[45]  Andrew P. Bradley,et al.  Perceptual quality metrics applied to still image compression , 1998, Signal Process..

[46]  Peter G. Engeldrum,et al.  Psychometric Scaling: A Toolkit for Imaging Systems Development , 2000 .

[47]  Shumeet Baluja,et al.  Expectation-based selective attention for visual monitoring and control of a robot vehicle , 1997, Robotics Auton. Syst..

[48]  Fabio Pellacini,et al.  Perceptually-driven decision theory for interactive realistic rendering , 2003, TOGS.

[49]  Jacob Cohen.  Statistical Power Analysis for the Behavioral Sciences , 1969, The SAGE Encyclopedia of Research Design.

[50]  Laurent Itti,et al.  Automatic foveation for video compression using a neurobiological model of visual attention , 2004, IEEE Transactions on Image Processing.

[51]  H. A. David,et al.  The method of paired comparisons , 1966 .

[52]  Tim Morris,et al.  Computer Vision and Image Processing: 4th International Conference, CVIP 2019, Jaipur, India, September 27–29, 2019, Revised Selected Papers, Part I , 2020, CVIP.

[53]  S Ullman,et al.  Shifts in selective visual attention: towards the underlying neural circuitry. , 1985, Human neurobiology.