skimage2.metrics.structural_similarity#

skimage2.metrics.structural_similarity(im1, im2, *, data_range, win_size=None, gradient=False, channel_axis=None, gaussian_weights=False, full=False, use_sample_covariance=True, K1=0.01, K2=0.03, sigma=1.5)[source]#

Compute the mean structural similarity index between two images.

Parameters:

im1, im2ndarray: Images. Any dimensionality with same shape.
data_rangefloat: The data range of the input image (difference between maximum and minimum possible values).
win_sizeint or None, optional: The side-length of the sliding window used in comparisons (default: 7). Must be an odd value. If gaussian_weights is True, win_size cannot be specified since the window size is then determined by sigma.
gradientbool, optional: If True, also return the gradient with respect to im2.
channel_axisint or None, optional: If None, the image is assumed to be a grayscale (single channel) image. Otherwise, this parameter indicates which axis of the array corresponds to channels.
gaussian_weightsbool, optional: If True, the local mean and variance are computed using a normalized Gaussian kernel of width sigma rather than a uniform window.
fullbool, optional: If True, also return the full structural similarity image.
use_sample_covariancebool, optional: If True, normalize covariances by N-1 rather than, N where N is the number of pixels within the sliding window.
K1float, optional: Algorithm parameter, K1 (small constant, see [1]).
K2float, optional: Algorithm parameter, K2 (small constant, see [1]).
sigmafloat, optional: Standard deviation for the Gaussian when gaussian_weights is True. Default is 1.5.

Returns:

mssimfloat: The mean structural similarity index over the image.
gradndarray: The gradient of the structural similarity between im1 and im2 [2]. This is only returned if gradient is set to True.
Sndarray: The full SSIM image. This is only returned if full is set to True.

Notes

To match the implementation of Wang et al. [1], set gaussian_weights to True, sigma to 1.5, use_sample_covariance to False, and specify the data_range argument.

References

[1] (1,2,3)

Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004). Image quality assessment: From error visibility to structural similarity. IEEE Transactions on Image Processing, 13, 600-612. https://ece.uwaterloo.ca/~z70wang/publications/ssim.pdf, DOI:10.1109/TIP.2003.819861

[2]

Avanaki, A. N. (2009). Exact global histogram specification optimized for structural similarity. Optical Review, 16, 613-621. arXiv:0901.0065 DOI:10.1007/s10043-009-0119-z

Examples

>>> import skimage as ski
>>> import _skimage2 as ski2

Structural similarity between identical images is 1.0 >>> im1 = ski2.data.camera() >>> structural_similarity(im1, im1.copy(), data_range=im1.max()) 1.0

Override part of the image with 0: >>> im2 = im1.copy() >>> im2[:30, :] = 0 >>> structural_similarity(im1, im2, data_range=im1.max()) # doctest: +ELLIPSIS 0.9408…