Universal Image Quality Index

Module Interface

class torchmetrics.image.UniversalImageQualityIndex(kernel_size=(11, 11), sigma=(1.5, 1.5), reduction='elementwise_mean', **kwargs)

Compute Universal Image Quality Index (UniversalImageQualityIndex).

As input to forward and update the metric accepts the following input

• preds (Tensor): Predictions from model of shape (N,C,H,W)

• target (Tensor): Ground truth values of shape (N,C,H,W)

As output of forward and compute the metric returns the following output

• uiqi (Tensor): if reduction!='none' returns float scalar tensor with average UIQI value over sample else returns tensor of shape (N,) with UIQI values per sample

Parameters:

• kernel_size (Sequence[int]) – size of the gaussian kernel

• sigma (Sequence[float]) – Standard deviation of the gaussian kernel

• reduction (Literal['elementwise_mean', 'sum', 'none', None]) –

  a method to reduce metric score over labels.

  • 'elementwise_mean': takes the mean (default)

  • 'sum': takes the sum

  • 'none' or None: no reduction will be applied

• kwargs (Any) – Additional keyword arguments, see Advanced metric settings for more info.

Returns:

Tensor with UniversalImageQualityIndex score

Example

>>> import torch
>>> from torchmetrics.image import UniversalImageQualityIndex
>>> preds = torch.rand([16, 1, 16, 16])
>>> target = preds * 0.75
>>> uqi = UniversalImageQualityIndex()
>>> uqi(preds, target)
tensor(0.9216)

plot(val=None, ax=None)

Plot a single or multiple values from the metric.

Parameters:

• val (Union[Tensor, Sequence[Tensor], None]) – Either a single result from calling metric.forward or metric.compute or a list of these results. If no value is provided, will automatically call metric.compute and plot that result.

• ax (Optional[Axes]) – An matplotlib axis object. If provided will add plot to that axis

Return type:

Tuple[Figure, Union[Axes, ndarray]]

Returns:

Figure and Axes object

Raises:

ModuleNotFoundError – If matplotlib is not installed

>>> # Example plotting a single value
>>> import torch
>>> from torchmetrics.image import UniversalImageQualityIndex
>>> preds = torch.rand([16, 1, 16, 16])
>>> target = preds * 0.75
>>> metric = UniversalImageQualityIndex()
>>> metric.update(preds, target)
>>> fig_, ax_ = metric.plot()

>>> # Example plotting multiple values
>>> import torch
>>> from torchmetrics.image import UniversalImageQualityIndex
>>> preds = torch.rand([16, 1, 16, 16])
>>> target = preds * 0.75
>>> metric = UniversalImageQualityIndex()
>>> values = [ ]
>>> for _ in range(10):
...     values.append(metric(preds, target))
>>> fig_, ax_ = metric.plot(values)

Functional Interface

torchmetrics.functional.image.universal_image_quality_index(preds, target, kernel_size=(11, 11), sigma=(1.5, 1.5), reduction='elementwise_mean')

Universal Image Quality Index.

Parameters:

• preds (Tensor) – estimated image

• target (Tensor) – ground truth image

• kernel_size (Sequence[int]) – size of the gaussian kernel

• sigma (Sequence[float]) – Standard deviation of the gaussian kernel

• reduction (Optional[Literal['elementwise_mean', 'sum', 'none']]) –

  a method to reduce metric score over labels.

  • 'elementwise_mean': takes the mean (default)

  • 'sum': takes the sum

  • 'none' or None: no reduction will be applied

Return type:

Tensor

Returns:

Tensor with UniversalImageQualityIndex score

Raises:

• TypeError – If preds and target don’t have the same data type.

• ValueError – If preds and target don’t have BxCxHxW shape.

• ValueError – If the length of kernel_size or sigma is not 2.

• ValueError – If one of the elements of kernel_size is not an odd positive number.

• ValueError – If one of the elements of sigma is not a positive number.

Example

>>> from torchmetrics.functional.image import universal_image_quality_index
>>> preds = torch.rand([16, 1, 16, 16])
>>> target = preds * 0.75
>>> universal_image_quality_index(preds, target)
tensor(0.9216)

References

[1] Zhou Wang and A. C. Bovik, “A universal image quality index,” in IEEE Signal Processing Letters, vol. 9, no. 3, pp. 81-84, March 2002, doi: 10.1109/97.995823.

[2] Zhou Wang, A. C. Bovik, H. R. Sheikh and E. P. Simoncelli, “Image quality assessment: from error visibility to structural similarity,” in IEEE Transactions on Image Processing, vol. 13, no. 4, pp. 600-612, April 2004, doi: 10.1109/TIP.2003.819861.