A multiresolution color model for visual difference prediction

ABSTRACT

How different are two images when viewed by a human observer? Such knowledge is needed in many situations including when one has to judge the degree to which a graphics representation may be similar to a high-quality photograph of the original scene. There is a class of computational models which attempt to predict such perceived differences. These are derived from theoretical considerations of human vision and are mostly validated from experiments on stimuli such as sinusoidal gratings. We are developing a model of visual difference prediction based on multi-scale analysis of local contrast, to be tested with psychophysical discrimination experiments on natural-scene stimuli. Here, we extend our model to account for differences in the chromatic domain. We describe the model, how it has been derived and how we attempt to validate it psychophysically for monochrome and chromatic images.

REFERENCES

• 1: Blakemore, C. and Campbell, F. W. 1969. On the existence of neurons in the human visual system selectively sensitive to the orientation and size of retinal images. Journal of Physiology-London 203, 237--260.
• 2: Scott Daly, The visible differences predictor: an algorithm for the assessment of image fidelity, Digital images and human vision, MIT Press, Cambridge, MA, 1993
• 3: De Valois, R. L., Albrecht, D. G. and Thorell, L. G. 1982. Spatial-frequency selectivity of cells in macaque visual cortex. Vision Research 22, 545--559.
• 4: Doll, T. J., Mcworter, S. W., Wasilewski, A. A. and Schmieder, D. E. 1998. Robust, sensor-independent target detection and recognition based on computational models of human vision. Optical Engineering 37, 2006--2021.
• 5: Hurvich, L. M. and Jameson, D. 1957. An opponent-process theory of color vision. Psychological review 64, 384--404.
• 6: Legge, G. E. 1981. A Power Law For Contrast Discrimination. Vision Research 21, 457--467.
• 7: Legge, G. E. and Foley, J. M. 1980. Contrast masking in human vision. Journal of the Optical Society of America 70, 1456--1471.
• 8: Lubin, J. 1995. A visual discrimination model for imaging system design and evaluation. Pp 245--283 In Vision Models for Target Detection and Recognition ed E. Peli, World Scientific: Singapore.
• 9: Macleod, D. I. A. and Boynton, R. M. 1979. Chromaticity diagram showing cone excitation by stimuli of equal luminance. Journal of the Optical Society of America A 68, 1183--1187.
• 10: Movshon, J. A., Thompson, I. D. and Tolhurst, D. J. 1978. Spatial and temporal contrast sensitivity of neurons in areas 17 and 18 of the cat's visual cortex. Journal of Physiology 283, 101--120.
• 11: Mullen, K. T. 1985. The contrast sensitivity of human color vision to red-green and blue-yellow chromatic gratings. Journal of Physiology-London 359, 381--400.
• 12: Mullen, K. T. and Losada, M. A. 1994. Evidence for separate pathways for color and luminance detection mechanisms. Journal of the Optical Society of America A, 11, 3136--3151.
• 13: Nachmias, J. and Sansbury, R. V. 1974. Grating contrast discrimination may be better than detection. Vision Research 14, 1039--1042.
• 14: Olmos, A. and Kingdom, F. A. A. 2004. A biologically inspired algorithm for the recovery of shading and reflectance images. Perception 33, 1463--1473.
• 15: Párraga, C. A. and Tolhurst, D. J. 2000. The effect of contrast randomisation on the discrimination of changes in the slopes of the amplitude spectra of natural scenes. Perception 29, 1101--1116.
• 16: Párraga, C. A., Troscianko, T. and Tolhurst, D. J. 2000. The human visual system is optimised for processing the spatial information in natural visual images. Current Biology 10, 35--38.
• 17: Párraga, C. A., Brelstaff, G., Troscianko, T. and Moorhead, I. R. 1998. Color and luminance information in natural scenes. Journal of the Optical Society of America A 15, 3, 563--569.
• 18: Párraga, C. A., Troscianko, T. and Tolhurst, D. J. 2005. The effects of amplitude-spectrum statistics on foveal and peripheral discrimination of changes in natural images, and a multi-resolution model. Vision Research submitted.
• 19: Peli, E. 1990. Contrast in Complex Images. Journal of the Optical Society of America A 7, 2032--2040.
• 20: Quick, R. F. 1974. A vector magnitude model of contrast detection. Kybernetik 16, 65--67.
• 21: Rohaly, A. M., Ahumada, A. J. and Watson, A. B. 1997. Object detection in natural backgrounds predicted by discrimination performance and models. Vision Research 37, 3225--3235.
• 22: Smith, V. C. and Pokorny, J. 1975. Spectral sensitivity of the foveal cone photopigments between 400 and 500 nm. Vision Research 15, 161--171.
• 23: Tadmor, Y. and Tolhurst, D. J. 1994. Discrimination of changes in the second-order statistics of natural and synthetic-images. Vision Research 34, 541--554.
• 24: Tolhurst, D. J. and Thompson, I. D. 1981. On the variety of spatial-frequency selectivities shown by neurons in area-17 of the cat. Proceedings of the Royal Society of London Series B-Biological Sciences 213, 183--199.
• 25: Watson, A. B. 1987. Efficiency of a Model Human Image Code. Journal of the Optical Society of America A 4, 2401--2417.
• 26: Watson, A. B. 2000. Visual detection of spatial contrast patterns: Evaluation of five simple models. Optical Express 6, 12--33.
• 27: Watson, A. B. and Robson, J. G. 1981. Discrimination at threshold: labelled detectors in human vision. Vision Research 21, 1115--1122.

INDEX TERMS

Primary Classification:
  J. Computer Applications
  J.2 PHYSICAL SCIENCES AND ENGINEERING

Additional Classification:
  J. Computer Applications
  J.4 SOCIAL AND BEHAVIORAL SCIENCES

Keywords:
color vision, image difference metrics, psychophysical testing