Function ssim

tensorflow/python/ops/image_ops_impl.py:3307–3378

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.

(img1,
         img2,
         max_val,
         filter_size=11,
         filter_sigma=1.5,
         k1=0.01,
         k2=0.03)

@tf_export('image.ssim')
def ssim(img1,
         img2,
         max_val,
         filter_size=11,
         filter_sigma=1.5,
         k1=0.01,
         k2=0.03):
  """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:

  ```python
      # 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, 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.
  ```

  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).
    filter_size: Default value 11 (size of gaussian filter).
    filter_sigma: Default value 1.5 (width of gaussian filter).
    k1: Default value 0.01
    k2: Default value 0.03 (SSIM is less sensitivity to K2 for lower values, so
      it would be better if we taken the values in range of 0< K2 <0.4).

  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]).
  """