skimage2.metrics.normalized_root_mse#

skimage2.metrics.normalized_root_mse(image_true, image_test, *, normalization='euclidean')[source]#

Compute the normalized root mean-squared error (NRMSE) between two images.

Parameters:
image_truendarray

Ground-truth image, same shape as im_test.

image_testndarray

Test image.

normalization{‘euclidean’, ‘min-max’, ‘mean’}, optional

Controls the normalization method to use in the denominator of the NRMSE. There is no standard method of normalization across the literature [1]. The methods available here are as follows:

  • ‘euclidean’ : normalize by the averaged Euclidean norm of im_true:

    NRMSE = RMSE * sqrt(N) / || im_true ||

    where || . || denotes the Frobenius norm and N = im_true.size. This result is equivalent to:

    NRMSE = || im_true - im_test || / || im_true ||.

  • ‘min-max’ : normalize by the intensity range of im_true.

  • ‘mean’ : normalize by the mean of im_true

Returns:
nrmsefloat

The NRMSE metric.

Notes

Changed in version 0.16: This function was renamed from skimage.measure.compare_nrmse to skimage.metrics.normalized_root_mse.

References