A model of perceptual image fidelity

The goal of numerous digital image processing algorithms is to reproduce an image as accurately as possible given some specific restrictions. For example, in digital image halftoning, a gray scale version of an image needs to be approximated by a high spatial resolution binary image. Likewise, lossy image compression seeks to reconstruct an image from a minimally coded description of the original. In these and many other applications, image fidelity is determined by the human observer; hence, the effectiveness of the algorithm is measured by the extent to which reproduction errors are visible. As a result, a model that predicts human perceptual sensitivity to image distortion is beneficial to both the design and evaluation of many such image processing algorithms. This summary briefly describes an extension of our work on perceptual image distortion. Our extended perceptual model accounts for: (1) contrast sensitivity as a function of spatial frequency, mean luminance and spatial extent, (2) luminance masking, and (3) contrast masking.

[1]  Andrew B. Watson,et al.  Visually optimal DCT quantization matrices for individual images , 1993, [Proceedings] DCC `93: Data Compression Conference.

[2]  Robert J. Safranek,et al.  Perceptual Coding of Image Signals , 1990, 1990 Conference Record Twenty-Fourth Asilomar Conference on Signals, Systems and Computers, 1990..

[3]  H. Richard Blackwell,et al.  IERI: Visual Performance Data for 156 Normal Observers of Various Ages , 1971 .

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

[5]  A B Watson,et al.  Perceptual-components architecture for digital video. , 1990, Journal of the Optical Society of America. A, Optics and image science.

[6]  Patrick C. Teo,et al.  Perceptual image distortion , 1994, Proceedings of 1st International Conference on Image Processing.

[7]  A B Watson,et al.  Efficiency of a model human image code. , 1987, Journal of the Optical Society of America. A, Optics and image science.

[8]  Jyrki Rovamo,et al.  Modelling the dependence of contrast sensitivity on grating area and spatial frequency , 1993, Vision Research.

[9]  Geoffrey M. Boynton,et al.  New model of human luminance pattern vision mechanisms: analysis of the effects of pattern orientation, spatial phase, and temporal frequency , 1994, Other Conferences.

[10]  Jeffrey Lubin,et al.  The use of psychophysical data and models in the analysis of display system performance , 1993 .

[11]  Gary E. Ford,et al.  Perceptually based coding of monochrome and color still images , 1992, Data Compression Conference, 1992..

[12]  Brian A. Wandell,et al.  Image Distortion Maps , 1997, Color Imaging Conference.

[13]  Philip T. Kortum,et al.  Adaptation mechanisms in spatial vision—ii. Flash thresholds and background adaptation , 1995, Vision Research.

[14]  Edward H. Adelson,et al.  Shiftable multiscale transforms , 1992, IEEE Trans. Inf. Theory.

[15]  J. M. Foley,et al.  Human luminance pattern-vision mechanisms: masking experiments require a new model. , 1994, Journal of the Optical Society of America. A, Optics, image science, and vision.

[16]  T.,et al.  Shiftable Multi-scale TransformsEero , 1992 .