viqa.fr_metrics.vif.VIFp

class viqa.fr_metrics.vif.VIFp(data_range=255, normalize=False, **kwargs)[source]

Calculate the visual information fidelity in pixel domain (VIFp) between two images.

score_val

VIFp score value of the last calculation.

Type:

float

parameters

Dictionary containing the parameters for VIFp calculation.

Type:

dict

Parameters:

  • data_range ({1, 255, 65535}, default=255) – Data range of the returned data in data loading. Is used for image loading when normalize is True and for the VIFp calculation. Passed to viqa.utils.load_data() and score().

  • normalize (bool, default=False) – If True, the input images are normalized to the data_range argument.

  • **kwargs (optional) – Additional parameters for data loading. The keyword arguments are passed to viqa.utils.load_data().

  • chromatic (bool, default False) –

    If True, the input images are expected to be RGB images.

    Note

    Color images can be used, but it is unclear how the called implementation piq.vif_p() handles the color channels.

Raises:

ValueError – If data_range is not set.

Warning

This metric is not yet tested. The metric should be only used for experimental purposes.

Notes

For more information on the VIFp metric, see [1].

References

score(img_r, img_m, dim=None, im_slice=None, **kwargs)[source]

Calculate the visual information fidelity in pixel domain (VIFp) between two images.

Parameters:

  • img_r (np.ndarray or Tensor or str or os.PathLike) – Reference image to calculate score against.

  • img_m (np.ndarray or Tensor or str or os.PathLike) – Distorted image to calculate score of.

  • dim ({0, 1, 2}, optional) – VIFp for 3D images is calculated as mean over all slices of the given dimension.

  • im_slice (int, optional) – If given, VIFp is calculated only for the given slice of the 3D image.

  • **kwargs (optional) – Additional parameters for VIFp calculation. The keyword arguments are passed to piq.vif_p(). See the documentation under [2].

  • sigma_n_sq (float, default=2.0) – HVS model parameter (variance of the visual noise). See [3].

  • reduction (str, default='mean') – Specifies the reduction type: ‘none’, ‘mean’ or ‘sum’.

Returns:

score_val – VIFp score value.

Return type:

float

Raises:

ValueError – If invalid dimension given in dim. If images are neither 2D nor 3D. If images are 3D, but dim is not given. If im_slice is given, but not an integer.

Warns:

RuntimeWarning – If dim or im_slice is given for 2D images.

If im_slice is not given, but dim is given for 3D images, VIFp is calculated for the full volume.

Notes

For 3D images if dim is given, but im_slice is not, the VIFp is calculated for the full volume of the 3D image. This is implemented as mean of the VIFp values of all slices of the given dimension. If dim is given and im_slice is given, the VIFp is calculated for the given slice of the given dimension (represents a 2D metric of the given slice).

References

print_score(decimals=2)[source]

Print the VIFp score value of the last calculation.

Parameters:

decimals (int, default=2) – Number of decimal places to print the score value.

Warns:

RuntimeWarning – If score_val is not available.

export_results(path, filename)

Export the score to a csv file.

Parameters:

  • path (str) – The path where the csv file should be saved.

  • filename (str) – The name of the csv file.

Notes

The arguments get passed to viqa.utils.export_results().

load_images(img_r, img_m)

Load the images and perform checks.

Parameters:

  • img_r (np.ndarray, viqa.ImageArray, torch.Tensor, str or os.PathLike) – The reference image.

  • img_m (np.ndarray, viqa.ImageArray, torch.Tensor, str or os.PathLike) – The modified image.

Returns: