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mindspore.nn.SSIM

class mindspore.nn.SSIM(max_val=1.0, filter_size=11, filter_sigma=1.5, k1=0.01, k2=0.03)

Returns SSIM index between two images.

Its implementation is based on 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.

SSIM is a measure of the similarity of two pictures. Like PSNR, SSIM is often used as an evaluation of image quality. SSIM is a number between 0 and 1.The larger it is, the smaller the gap between the output image and the undistorted image, that is, the better the image quality. When the two images are exactly the same, SSIM=1.

$$\begin{array}{rl}l(x,y)& =\frac{2{\mu }_x{\mu }_y+C_1}{{\mu }_x^2+{\mu }_y^2+C_1},C_1=(K_{1}L{)}^2.\\ c(x,y)& =\frac{2{\sigma }_x{\sigma }_y+C_2}{{\sigma }_x^2+{\sigma }_y^2+C_2},C_2=(K_{2}L{)}^2.\\ s(x,y)& =\frac{{\sigma }_{xy}+C_3}{{\sigma }_x{\sigma }_y+C_3},C_3=C_2/2.\\ SSIM(x,y)& =l\ast c\ast s\\ & =\frac{(2{\mu }_x{\mu }_y+C_1)(2{\sigma }_{xy}+C_2}{({\mu }_x^2+{\mu }_y^2+C_1)({\sigma }_x^2+{\sigma }_y^2+C_2)}.\end{array}$$

Parameters

• max_val (Union[int, float]) – The dynamic range of the pixel values (255 for 8-bit grayscale images). Default: 1.0.

• filter_size (int) – The size of the Gaussian filter. Default: 11. The value must be greater than or equal to 1.

• filter_sigma (float) – The standard deviation of Gaussian kernel. Default: 1.5. The value must be greater than 0.

• k1 (float) – The constant used to generate c1 in the luminance comparison function. Default: 0.01.

• k2 (float) – The constant used to generate c2 in the contrast comparison function. Default: 0.03.

Inputs:

• img1 (Tensor) - The first image batch with format ‘NCHW’. It must be the same shape and dtype as img2.

• img2 (Tensor) - The second image batch with format ‘NCHW’. It must be the same shape and dtype as img1.

Outputs:

Tensor, has the same dtype as img1. It is a 1-D tensor with shape N, where N is the batch num of img1.

Raises

• TypeError – If max_val is neither int nor float.

• TypeError – If k1, k2 or filter_sigma is not a float.

• TypeError – If filter_size is not an int.

• ValueError – If max_val or filter_sigma is less than or equal to 0.

• ValueError – If filter_size is less than 0.

Supported Platforms:

Ascend GPU CPU

Examples

>>> import numpy as np
>>> import mindspore.nn as nn
>>> from mindspore import Tensor
>>> net = nn.SSIM()
>>> img1 = Tensor(np.ones([1, 3, 16, 16]).astype(np.float32))
>>> img2 = Tensor(np.ones([1, 3, 16, 16]).astype(np.float32))
>>> output = net(img1, img2)
>>> print(output)
[1.]