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"""
===========================
Structural similarity index
===========================
When comparing images, the mean squared error (MSE)--while simple to
implement--is not highly indicative of perceived similarity. Structural
similarity aims to address this shortcoming by taking texture into account
[1]_, [2]_.
The example shows two modifications of the input image, each with the same MSE,
but with very different mean structural similarity indices.
.. [1] Zhou Wang; Bovik, A.C.; ,"Mean squared error: Love it or leave it? A new
look at Signal Fidelity Measures," Signal Processing Magazine, IEEE,
vol. 26, no. 1, pp. 98-117, Jan. 2009.
.. [2] Z. Wang, A. C. Bovik, H. R. Sheikh and E. P. Simoncelli, "Image quality
assessment: From error visibility to structural similarity," IEEE
Transactions on Image Processing, vol. 13, no. 4, pp. 600-612,
Apr. 2004.
"""
import numpy as np
import matplotlib.pyplot as plt
from skimage import data, img_as_float
from skimage.metrics import structural_similarity as ssim
from skimage.metrics import mean_squared_error
img = img_as_float(data.camera())
rows, cols = img.shape
noise = np.ones_like(img) * 0.2 * (img.max() - img.min())
rng = np.random.default_rng()
noise[rng.random(size=noise.shape) > 0.5] *= -1
img_noise = img + noise
img_const = img + abs(noise)
fig, axes = plt.subplots(nrows=1, ncols=3, figsize=(10, 4), sharex=True, sharey=True)
ax = axes.ravel()
mse_none = mean_squared_error(img, img)
ssim_none = ssim(img, img, data_range=img.max() - img.min())
mse_noise = mean_squared_error(img, img_noise)
ssim_noise = ssim(img, img_noise, data_range=img_noise.max() - img_noise.min())
mse_const = mean_squared_error(img, img_const)
ssim_const = ssim(img, img_const, data_range=img_const.max() - img_const.min())
ax[0].imshow(img, cmap=plt.cm.gray, vmin=0, vmax=1)
ax[0].set_xlabel(f'MSE: {mse_none:.2f}, SSIM: {ssim_none:.2f}')
ax[0].set_title('Original image')
ax[1].imshow(img_noise, cmap=plt.cm.gray, vmin=0, vmax=1)
ax[1].set_xlabel(f'MSE: {mse_noise:.2f}, SSIM: {ssim_noise:.2f}')
ax[1].set_title('Image with noise')
ax[2].imshow(img_const, cmap=plt.cm.gray, vmin=0, vmax=1)
ax[2].set_xlabel(f'MSE: {mse_const:.2f}, SSIM: {ssim_const:.2f}')
ax[2].set_title('Image plus constant')
plt.tight_layout()
plt.show()
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