tf.image.psnr

Returns the Peak Signal-to-Noise Ratio between a and b.

tf.image.psnr( a, b, max_val, name=None )

Used in the notebooks

Used in the tutorials

This is intended to be used on signals (or images). Produces a PSNR value for each image in batch.

The last three dimensions of input are expected to be [height, width, depth].

Example:

 # Read images from file. im1 = tf.decode_png('path/to/im1.png') im2 = tf.decode_png('path/to/im2.png') # Compute PSNR over tf.uint8 Tensors. psnr1 = tf.image.psnr(im1, im2, max_val=255) # Compute PSNR over tf.float32 Tensors. im1 = tf.image.convert_image_dtype(im1, tf.float32) im2 = tf.image.convert_image_dtype(im2, tf.float32) psnr2 = tf.image.psnr(im1, im2, max_val=1.0) # psnr1 and psnr2 both have type tf.float32 and are almost equal.

a First set of images.
b Second set of images.
max_val The dynamic range of the images (i.e., the difference between the maximum the and minimum allowed values).
name Namespace to embed the computation in.

The scalar PSNR between a and b. The returned tensor has type tf.float32 and shape [batch_size, 1].