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.

Traceback (most recent call last):
  File "/build/skimage-Lp2Zl4/skimage-0.16.2/doc/examples/transform/plot_ssim.py", line 1
    ===========================
    ^
SyntaxError: invalid syntax

===========================
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


img = img_as_float(data.camera())
rows, cols = img.shape

noise = np.ones_like(img) * 0.2 * (img.max() - img.min())
noise[np.random.random(size=noise.shape) > 0.5] *= -1

def mse(x, y):
    return np.linalg.norm(x - y)

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 = mse(img, img)
ssim_none = ssim(img, img, data_range=img.max() - img.min())

mse_noise = mse(img, img_noise)
ssim_noise = ssim(img, img_noise,
                  data_range=img_noise.max() - img_noise.min())

mse_const = mse(img, img_const)
ssim_const = ssim(img, img_const,
                  data_range=img_const.max() - img_const.min())

label = 'MSE: {:.2f}, SSIM: {:.2f}'

ax[0].imshow(img, cmap=plt.cm.gray, vmin=0, vmax=1)
ax[0].set_xlabel(label.format(mse_none, ssim_none))
ax[0].set_title('Original image')

ax[1].imshow(img_noise, cmap=plt.cm.gray, vmin=0, vmax=1)
ax[1].set_xlabel(label.format(mse_noise, ssim_noise))
ax[1].set_title('Image with noise')

ax[2].imshow(img_const, cmap=plt.cm.gray, vmin=0, vmax=1)
ax[2].set_xlabel(label.format(mse_const, ssim_const))
ax[2].set_title('Image plus constant')

plt.tight_layout()
plt.show()

Total running time of the script: ( 0 minutes 0.000 seconds)