import torch
from torch import Tensor
[docs]def psnr(image: Tensor, target: Tensor) -> Tensor:
r"""
Computes the peak signal-to-noise ratio on a batch of images.
Args:
image (Tensor): Input images in format :math:`(N, C, H, W)`.
target (Tensor): Target images in format :math:`(N, C, H, W)`.
Returns:
Tensor: PSNR for each image in the batch, of shape :math:`(N)`.
Note:
Peak signal-to-noise ratio is an inverse log scale of the mean squared error
measured in decibels. The formula used here is
.. math::
P(x, y) = 10 \log_{10}\left(\frac{1}{\text{MSE}(x, y)}\right)
"""
mse = torch.nn.functional.mse_loss(image, target, reduction="none")
mse = mse.view(mse.shape[0], -1).mean(1)
return 10 * torch.log10(1 / mse)
