risk-methods: Compute risks characterizing the quality of smoothing results
In WIAS-BERLIN/aws: Adaptive Weights Smoothing

risk-methods

R Documentation

Compute risks characterizing the quality of smoothing results

Description

Methods function risk in package aws. For an given array u the following statistics are computed : Root Mean Squared Error RMSE <- sqrt(mean((y-u)^2)), Signal to Noise Ratio SNR <- 10*log(mean(u^2)/MSE,10), Peak Signal to Noise Ratio PSNR <- 10*log(max(u^2)/MSE,10), Mean Absolute Error MAE <- mean(abs(y-u)), Maximal Absolute Error MaxAE <- max(abs(y-u)), Universal Image Quality Index (UIQI) (Wang and Bovik (2002)).

Usage

  ## S4 method for signature 'array'
risk(y, u=0)
  ## S4 method for signature 'aws'
risk(y, u=0)
  ## S4 method for signature 'awssegment'
risk(y, u=0)
  ## S4 method for signature 'ICIsmooth'
risk(y, u=0)
  ## S4 method for signature 'kernsm'
risk(y, u=0)
  ## S4 method for signature 'numeric'
risk(y, u=0)

Arguments

`y`	object
`u`	array of dimension `dim(y)` or `dim(extract(y,what="yhat")$y)` or scalar value used in comparisons.

Methods

signature(y = "ANY"): The method extract and/or compute specified statistics from object of class
signature(y = "array"): Returns a list with components RMSE, SNR, PSNR, MAE, MaxAE, UIQI
signature(y = "aws"): Returns a list with components RMSE, SNR, PSNR, MAE, MaxAE, UIQI
signature(y = "awssegment"): Returns a list with components RMSE, SNR, PSNR, MAE, MaxAE, UIQI
signature(y = "ICIsmooth"): Returns a list with components RMSE, SNR, PSNR, MAE, MaxAE, UIQI
signature(y = "kernsm"): Returns a list with components RMSE, SNR, PSNR, MAE, MaxAE, UIQI
signature(y = "numeric"): Returns a list with components RMSE, SNR, PSNR, MAE, MaxAE, UIQI

Author(s)

Joerg Polzehl polzehl@wias-berlin.de

References

V. Katkovnik, K. Egiazarian and J. Astola, Local Approximation Techniques in Signal And Image Processing, SPIE Society of Photo-Optical Instrumentation Engin., 2006, PM157

Z. Wang and A. C. Bovik, A universal image quality index, IEEE Signal Processing Letters, vol. 9, N3, pp. 81-84, 2002.

WIAS-BERLIN/aws documentation built on Sept. 10, 2023, 6:20 p.m.