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# tf.image.ssim_multiscale

Computes the MS-SSIM between img1 and img2.

``````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].

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

#### Arguments:

• `img1`: First image batch.
• `img2`: Second image batch. 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 taken the values in 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]).