viqa.fr_metrics.fsim.FSIM¶

class viqa.fr_metrics.fsim.FSIM(data_range=255, normalize=False, **kwargs)[source]¶

Calculate the feature similarity (FSIM) between two images.

score_val¶

FSIM score value of the last calculation.

Type:: float

parameters¶

Dictionary containing the parameters for FSIM 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 FSIM 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 and FSIMc is calculated. See [1]. Passed to piq.fsim(). See the documentation under [2].

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 FSIM metric, see [1].

References

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

Calculate the FSIM score between two images.

The metric can be calculated for 2D and 3D images. If the images are 3D, the metric can be calculated for the full volume or for a given slice of the image by setting dim to the desired dimension and im_slice to the desired slice number.

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) – FSIM for 3D images is calculated as mean over all slices of the given dimension.
im_slice (int, optional) – If given, FSIM is calculated only for the given slice of the 3D image.
**kwargs (optional) – Additional parameters for FSIM calculation. The keyword arguments are passed to piq.fsim(). See the documentation under [3].
reduction (str, default='mean') – Specifies the reduction type: ‘none’, ‘mean’ or ‘sum’.
scales (int, default=4) – Number of wavelets used for computation of phase congruency maps.
orientations (int, default=4) – Number of filter orientations used for computation of phase congruency maps.
min_length (int, default=6) – Wavelength of smallest scale filter.
mult (int, default=2) – Scaling factor between successive filters.
sigma_f (float, default=0.55) – Ratio of the standard deviation of the Gaussian describing the log Gabor filter’s transfer function in the frequency domain to the filter center frequency.
delta_theta (float, default=1.2) – Ratio of angular interval between filter orientations and the standard deviation of the angular Gaussian function used to construct filters in the frequency plane.
k (float, default=2.0) – No of standard deviations of the noise energy beyond the mean at which we set the noise threshold point, below which phase congruency values get penalized.