tf.image.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.
# 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).
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]).
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Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.