General Description
This metric is built into the models for non-homogeneous backgrounds. The metric takes as input a luminance image. The image is blurred by an 'optical' blur function and then blurred again to generate a local luminance image. These are combined pixel by pixel to form a visible contrast image.
Metric applications
1) Visible contrast energy
2) Discriminability models
Capabilities and limitations
1) Images need not be square or have square pixels
2) No provision for visual field inhomogeneity.
System requirements: Mathematica version tested on Mathematica 2.2 for SPARC. Matlab version tested on Matlab 5.2 for MacIntosh.
Data requirements
Input data
1) Number of rows and columns in image
2) Pixels per degree of visual angle in row and column directions
3) Luminance values for image
4) Contrast sensitivity parameters
Data format and units
1) Images are assumed to be rectangular arrays.
2) Luminance units do not matter, they are divided out.
Metric output data
Visible contrast image (possibly negative or infinite)
References
Ahumada (2005 ECVP)
A local contrast metric.
Ahumada and Beard
(1998 SID)
A simple vision model for inhomogeneous image quality assessment.
Ahumada, Beard, and Jones
(2005 VSS)
Modeling the detection of blurred visual targets in non-homogeneous
backgrounds.