CQ: Codispersion based similarity index

In SpatialPack: Tools for Assessment the Association Between Two Spatial Processes

Codispersion based similarity index

Description

This function computes a similarity index (CQ) based on the codispersion coefficient.

Usage

CQ(x, y, h = c(0,1), eps = c(0.01, 0.03), L = 255)

Arguments

x	reference image matrix (grayscale)
y	distorted image matrix (grayscale)
h	2-dimensional vector of the spatial lag. Default value is h = c(0,1).
eps	rescaling constants, by default eps = c(0.01, 0.03)
L	dynamic range of the images, by default L = 255.

Value

A list containing the following components:

CQ	codispersion based similarity index between images x and y.
direction	vector of spatial lag.
comps	components of CQ, that is luminance, contrast and codispersion
stats	sample statistics (means, variances and covariance) for each image.
speed	Running time taken by the procedure.

References

Ojeda, S.M., Lamberti, P.W., Vallejos, R. (2012). Measure of similarity between images based on the codispersion coefficient. Journal of Electronic Imaging 21, 023019.

Vallejos, R., Mancilla, D., Acosta, J. (2016). Image similarity assessment based on coefficients of spatial association. Journal of Mathematical Imaging and Vision 56, 77-98.

Examples

data(texmos2)

y <- imnoise(texmos2, type = "gaussian")
plot(as.raster(y))
o <- CQ(texmos2, y, h = c(0,1))
o

y <- imnoise(texmos2, type = "speckle")
plot(as.raster(y))
o <- CQ(texmos2, y, h = c(0,1))
o

SpatialPack documentation built on Aug. 19, 2022, 5:20 p.m.