PSSIM_snow: Image structural similarity measure PSSIM based on hypothesis...

In PSSIM: Test of Independence & Image Structural Similarity Measure PSSIM

Description

PSSIM_snow computes image structural similarity PSSIM of Wang, Maldonado and Silwal (2011) using parallel programming.

Usage

PSSIM_snow(
  A,
  A1,
  nprocess = min(8, parallel::detectCores()),
  b = 64,
  a = 2,
  vs = 32,
  wavecoeff = FALSE,
  cs = 2,
  dyn = FALSE
)

Arguments

A	a grayscale image stored as a matrix.
A1	grayscale image stored as a matix. Same dimension as A.
nprocess	number of cores (workers) to use for parallel computation. Note: In personal computer, nprocess =detectCores() is good to use. On cluster machine, nprocess need to be specified to a number that is no more than its number of cores (for courtesy)
b	Number of columns in each block. Suggest to use default value 64.
a	Number of rows in each block. Suggest to use default value 2.
vs	Block shift size. Suggest to use default value 32.
wavecoeff	logical of whether the input matrices are wavelet coefficients. Currently, wavelet version is not implemented. This parameter is a placeholder for future implementation.
cs	dividing factor to split index.
dyn	logical, whether dynamic scheduling should be used.

Value

: Image structural similarity based on PSSIM. The value is in [0,1] with values close to 0 meaning the two images are different and values close to 1 meaning the two iamges are similar.

References

Haiyan Wang, Diego Maldonado, and Sharad Silwal (2011). A Nonparametric-Test-Based Structural Similarity Measure for Digital Images. Computational Statistics and Data Analysis. 55: 2925-2936. Doi:10.1016/j.csda.2011.04.021

Examples

  A=miniimagematrix$A
  B=miniimagematrix$B
  # see it with image(A, axes=FALSE, col  = gray((0:255)/256) )
  PSSIM_snow(A, B, nprocess=2)

PSSIM documentation built on Sept. 13, 2020, 5:18 p.m.