compare-ssim

compare_ssim

skimage.measure.compare_ssim(X, Y, win_size=None, gradient=False, dynamic_range=None, multichannel=False, gaussian_weights=False, full=False, **kwargs) [source]

Compute the mean structural similarity index between two images.

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 If True, also return the gradient. dynamic_range : int The dynamic range of the input image (distance between minimum and maximum possible values). By default, this is estimated from the image data-type. multichannel : int or None If True, treat the last dimension of the array as channels. Similarity calculations are done independently for each channel then averaged. gaussian_weights : bool If True, each patch has its mean and variance spatially weighted by a normalized Gaussian kernel of width sigma=1.5. full : bool If True, return the full structural similarity image instead of the mean value
Returns:	mssim : float or ndarray The mean structural similarity over the image. grad : ndarray The gradient of the structural similarity index between X and Y [R262]. 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 [R261]) K2 : float algorithm parameter, K2 (small constant, see [R261]) sigma : float sigma for the Gaussian when gaussian_weights is True.

Notes

To match the implementation of Wang et. al. [R261], set gaussian_weights to True, sigma to 1.5, and use_sample_covariance to False.

References

[R261]

(1, 2, 3, 4) 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

[R262]

(1, 2) Avanaki, A. N. (2009). Exact global histogram specification optimized for structural similarity. Optical Review, 16, 613-621. http://arxiv.org/abs/0901.0065