Shortcuts

Universal Image Quality Index

Module Interface

class torchmetrics.UniversalImageQualityIndex(kernel_size=(11, 11), sigma=(1.5, 1.5), reduction='elementwise_mean', data_range=None, **kwargs)[source]

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

  • data_range (Optional[float]) – Range of the image. If None, it is determined from the image (max - min)

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

Returns

Tensor with UniversalImageQualityIndex score

Example

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

Initializes internal Module state, shared by both nn.Module and ScriptModule.

Functional Interface

torchmetrics.functional.universal_image_quality_index(preds, target, kernel_size=(11, 11), sigma=(1.5, 1.5), reduction='elementwise_mean', data_range=None)[source]

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

  • data_range (Optional[float]) – Range of the image. If None, it is determined from the image (max - min)

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 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.

Read the Docs v: latest
Versions
latest
stable
v0.11.1
v0.11.0
v0.10.3
v0.10.2
v0.10.1
v0.10.0
v0.9.3
v0.9.2
v0.9.1
v0.9.0
v0.8.2
v0.8.1
v0.8.0
v0.7.3
v0.7.2
v0.7.1
v0.7.0
v0.6.2
v0.6.1
v0.6.0
v0.5.1
v0.5.0
v0.4.1
v0.4.0
v0.3.2
v0.3.1
v0.3.0
v0.2.0
v0.1.0
Downloads
pdf
html
On Read the Docs
Project Home
Builds

Free document hosting provided by Read the Docs.