# tf.image.ssim_multiscale

tf.image.ssim_multiscale(
img1,
img2,
max_val,
power_factors=_MSSSIM_WEIGHTS
)


Computes the MS-SSIM between img1 and img2.

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.

#### 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]).