tf.image.ssim(img1,img2,max_val)
Defined in?tensorflow/python/ops/image_ops_impl.py.
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.
Args:
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]).
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https://tensorflow.google.cn/api_docs/python/tf/image/ssim?hl=zh-cn
