tf.image.ssim_multiscale

Computes the MS-SSIM between img1 and img2.

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tf.compat.v1.image.ssim_multiscale

tf.image.ssim_multiscale(
    img1,
    img2,
    max_val,
    power_factors=_MSSSIM_WEIGHTS,
    filter_size=11,
    filter_sigma=1.5,
    k1=0.01,
    k2=0.03
)

This function assumes that img1 and img2 are image batches, i.e. the last three dimensions are [height, width, channels].

Note: The true SSIM is only defined on grayscale. This function does not perform any colorspace transform. (If the input is already YUV, then it will compute YUV SSIM average.)

Original paper: Wang, Zhou, Eero P. Simoncelli, and Alan C. Bovik. "Multiscale structural similarity for image quality assessment." Signals, Systems and Computers, 2004.

Args
img1	First image batch with only Positive Pixel Values.
img2	Second image batch with only Positive Pixel Values. Must have the same rank as img1.
max_val	The dynamic range of the images (i.e., the difference between the maximum the and minimum allowed values).
power_factors	Iterable of weights for each of the scales. The number of scales used is the length of the list. Index 0 is the unscaled resolution's weight and each increasing scale corresponds to the image being downsampled by 2. Defaults to (0.0448, 0.2856, 0.3001, 0.2363, 0.1333), which are the values obtained in the original paper.
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).

Returns
A tensor containing an MS-SSIM value for each image in batch. The values are in range [0, 1]. Returns a tensor with shape: broadcast(img1.shape[:-3], img2.shape[:-3]).