Parameters:

im1, im2 : ndarray

Image. Any dimensionality.

Returns:

mse : float

The mean-squared error (MSE) metric.

Parameters:

im_true : ndarray

Ground-truth image.

im_test : ndarray

Test image.

data_range : int

The data range of the input image (distance between minimum and maximum possible values). By default, this is estimated from the image data-type.

Returns:

psnr : float

The PSNR metric.

Parameters:

X, Y : ndarray

Image. Any dimensionality.

win_size : int or None

The side-length of the sliding window used in comparison. Must be an odd value. If gaussian_weights is True, this is ignored and the window size will depend on sigma.

gradient : bool, optional

If True, also return the gradient.

data_range : int, optional

The data range of the input image (distance between minimum and maximum possible values). By default, this is estimated from the image data-type.

multichannel : bool, optional

If True, treat the last dimension of the array as channels. Similarity calculations are done independently for each channel then averaged.

gaussian_weights : bool, optional

If True, each patch has its mean and variance spatially weighted by a normalized Gaussian kernel of width sigma=1.5.

full : bool, optional

If True, return the full structural similarity image instead of the mean value.

Returns:

mssim : float

The mean structural similarity over the image.

grad : ndarray

The gradient of the structural similarity index between X and Y [R327]. This is only returned if gradient is set to True.

S : ndarray

The full SSIM image. This is only returned if full is set to True.

Other Parameters:

use_sample_covariance : bool

if True, normalize covariances by N-1 rather than, N where N is the number of pixels within the sliding window.

K1 : float

algorithm parameter, K1 (small constant, see [R326])

K2 : float

algorithm parameter, K2 (small constant, see [R326])

sigma : float

sigma for the Gaussian when gaussian_weights is True.