SSIM是一种衡量两幅图片相似度的指标。 出处来自于2004年的一篇TIP， 标题为：Image Quality Assessment: From Error Visibility to Structural Similarity 地址为：https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1284395
与PSNR一样，SSIM也经常用作图像质量的评价。
先了解SSIM的输入 SSIM的输入就是两张图像，我们要得到其相似性的两张图像。其中一张是未经压缩的无失真图像(即ground truth)，另一张就是你恢复出的图像。所以，SSIM可以作为super-resolution质量的指标。 假设我们输入的两张图像分别是x和y，那么
式1是SSIM的数学定义，其中：
总结：
- SSIM具有对称性，即SSIM(x,y)=SSIM(y,x)
- SSIM是一个0到1之间的数，越大表示输出图像和无失真图像的差距越小，即图像质量越好。当两幅图像一模一样时，SSIM=1；
如PSNR一样，SSIM这种常用计算函数也被tensorflow收编了，我们只需在tf中调用ssim就可以了：
tf.image.ssim(x, y, 255)源代码如下：
def ssim(img1, img2, max_val):
"""Computes SSIM index between img1 and img2.
This function is based on the standard SSIM implementation from:
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.
Note: The true SSIM is only defined on grayscale. This function does not
perform any colorspace transform. (If input is already YUV, then it will
compute YUV SSIM average.)
Details:
- 11x11 Gaussian filter of width 1.5 is used.
- k1 = 0.01, k2 = 0.03 as in the original paper.
The image sizes must be at least 11x11 because of the filter size.
Example:
# Read images from file.
im1 = tf.decode_png('path/to/im1.png')
im2 = tf.decode_png('path/to/im2.png')
# Compute SSIM over tf.uint8 Tensors.
ssim1 = tf.image.ssim(im1, im2, max_val=255)
# Compute SSIM over tf.float32 Tensors.
im1 = tf.image.convert_image_dtype(im1, tf.float32)
im2 = tf.image.convert_image_dtype(im2, tf.float32)
ssim2 = tf.image.ssim(im1, im2, max_val=1.0)
# ssim1 and ssim2 both have type tf.float32 and are almost equal.
img1: First image batch.
img2: Second image batch.
max_val: The dynamic range of the images (i.e., the difference between the
maximum the and minimum allowed values).
Returns:
A tensor containing an SSIM value for each image in batch. Returned SSIM
values are in range (-1, 1], when pixel values are non-negative. Returns
a tensor with shape: broadcast(img1.shape[:-3], img2.shape[:-3]).
"""
_, _, checks = _verify_compatible_image_shapes(img1, img2)
with ops.control_dependencies(checks):
img1 = array_ops.identity(img1)
# Need to convert the images to float32. Scale max_val accordingly so that
# SSIM is computed correctly.
max_val = math_ops.cast(max_val, img1.dtype)
max_val = convert_image_dtype(max_val, dtypes.float32)
img1 = convert_image_dtype(img1, dtypes.float32)
img2 = convert_image_dtype(img2, dtypes.float32)
ssim_per_channel, _ = _ssim_per_channel(img1, img2, max_val)
# Compute average over color channels.
return math_ops.reduce_mean(ssim_per_channel, [-1])参考：https://en.wikipedia.org/wiki/Structural_similarity
