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/TensorFlow Python

# tf.image.ssim

```tf.image.ssim(
img1,
img2,
max_val
)
```

Computes SSIM index between img1 and img2.

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)

# 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)
# 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).

#### 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]).