tf.image.ssim

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Computes SSIM index between img1 and img2.

Aliases:

  • tf.compat.v1.image.ssim
  • tf.compat.v2.image.ssim
tf.image.ssim(
    img1,
    img2,
    max_val,
    filter_size=11,
    filter_sigma=1.5,
    k1=0.01,
    k2=0.03
)

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, 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]).

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