Visual assessment of variable-resolution imagery.

A technique is described for producing variable-resolution images whose spatial detail decreases as a function of distance from their centers. These images can be matched in some sense to the normal spatial inhomogeneities of the human visual system, as well as to various abnormalities in spatial discrimination. A set of images was generated with a series of linear distortion functions whose low-pass characteristics differed at both the center and the periphery of the image as well as across the image. A forced-choice procedure was used to determine which test images were indistinguishable from unprocessed versions of themselves. Certain of the threshold distortion functions are compared with eccentricity scaling functions that have been used by others to characterize various aspects of peripheral vision. Finally, the concept of locally band-limited spaces is discussed, and an efficient sampling technique based on the concept is described. This technique can be used to generate an image that, under certain conditions, is visually equivalent to an otherwise identical image containing significantly more information.

[1]  Lotfi A. Zadeh,et al.  A general theory of linear signal transmission systems , 1952 .

[2]  D J Field,et al.  Relations between the statistics of natural images and the response properties of cortical cells. , 1987, Journal of the Optical Society of America. A, Optics and image science.

[3]  Touradj Ebrahimi,et al.  Image compression by Gabor expansion , 1991 .

[4]  Yehoshua Y. Zeevi,et al.  Pyramidal Image Representation In Nonuniform Systems , 1988, Other Conferences.

[5]  R. Baddeley,et al.  A statistical analysis of natural images matches psychophysically derived orientation tuning curves , 1991, Proceedings of the Royal Society of London. Series B: Biological Sciences.

[6]  Yehoshua Y. Zeevi,et al.  Nonuniform image representation in area-of-interest systems , 1995, IEEE Trans. Image Process..

[7]  Kazuo Horiuchi,et al.  Sampling Principle for Continuous Signals with Time-Varying Bands , 1968, Inf. Control..

[8]  Philip Hobsbaum A theory of communication , 1970 .

[9]  R Näsänen,et al.  Cortical magnification and peripheral vision. , 1987, Journal of the Optical Society of America. A, Optics and image science.

[10]  John H. R. Maunsell,et al.  The visual field representation in striate cortex of the macaque monkey: Asymmetries, anisotropies, and individual variability , 1984, Vision Research.

[11]  S. Klein,et al.  Vernier acuity, crowding and cortical magnification , 1985, Vision Research.

[12]  H Strasburger,et al.  Cortical Magnification Theory Fails to Predict Visual Recognition , 1994, The European journal of neuroscience.

[13]  Yehoshua Y. Zeevi,et al.  Nonuniform sampling and antialiasing in image representation , 1993, IEEE Trans. Signal Process..

[14]  E. Peli,et al.  Image invariance with changes in size: the role of peripheral contrast thresholds. , 1991, Journal of the Optical Society of America. A, Optics and image science.

[15]  W. Charman,et al.  Off-axis image quality in the human eye , 1981, Vision Research.

[16]  A. J. Jerri The Shannon sampling theorem—Its various extensions and applications: A tutorial review , 1977, Proceedings of the IEEE.

[17]  James J. Clark,et al.  A transformation method for the reconstruction of functions from nonuniformly spaced samples , 1985, IEEE Trans. Acoust. Speech Signal Process..

[18]  Y. Zeevi,et al.  Preattentive equivalence of multicomponent Gabor textures in the central and peripheral visual field , 1995, Vision Research.

[19]  Touradj Ebrahimi,et al.  New generation methods for the high-compression coding of digital image sequences , 1990 .