tf.image.ssim

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
)

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.

  • 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 (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.

img1 First image batch. 4-D Tensor of shape [batch, height, width, channels] with only Positive Pixel Values.
img2 Second image batch. 4-D Tensor of shape [batch, height, width, channels] with only Positive Pixel Values.
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 took the values in the range of 0 < K2 < 0.4).

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]).