View source on GitHub 
Computes SSIM index between img1 and img2.
tf.image.ssim(
img1,
img2,
max_val,
filter_size=11,
filter_sigma=1.5,
k1=0.01,
k2=0.03,
return_index_map=False
)
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.
Details  


The image sizes must be at least 11x11 because of the filter size.
Example:
# Read images (of size 255 x 255) from file.
im1 = tf.image.decode_image(tf.io.read_file('path/to/im1.png'))
im2 = tf.image.decode_image(tf.io.read_file('path/to/im2.png'))
tf.shape(im1) # `img1.png` has 3 channels; shape is `(255, 255, 3)`
tf.shape(im2) # `img2.png` has 3 channels; shape is `(255, 255, 3)`
# Add an outer batch for each image.
im1 = tf.expand_dims(im1, axis=0)
im2 = tf.expand_dims(im2, axis=0)
# Compute SSIM over tf.uint8 Tensors.
ssim1 = tf.image.ssim(im1, im2, max_val=255, filter_size=11,
filter_sigma=1.5, k1=0.01, k2=0.03)
# 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, filter_size=11,
filter_sigma=1.5, k1=0.01, k2=0.03)
# ssim1 and ssim2 both have type tf.float32 and are almost equal.
Returns  

A tensor containing an SSIM value for each image in batch or a tensor containing an SSIM value for each pixel for each image in batch if return_index_map is True. Returned SSIM values are in range (1, 1], when pixel values are nonnegative. Returns a tensor with shape: broadcast(img1.shape[:3], img2.shape[:3]) or broadcast(img1.shape[:1], img2.shape[:1]). 