code: def pamse(vref, vcmp, rescale=True):
      
  
      # Calculate difference, promoting to float if vref and vcmp have integer
      # dtype
      emap = np.asarray(vref, dtype=np.float64) - \
          np.asarray(vcmp, dtype=np.float64)
      # Input images in reference code on which this implementation is
      # based are assumed to be on range [0,...,255].
      if rescale:
          emap *= (255.0 / vref.max())
      sigma = 0.8
      herr = ndimage.filters.gaussian_filter(emap, sigma)
      score = np.mean(herr**2)
      return score
docs: Compute Perceptual-fidelity Aware Mean Squared Error (PAMSE) IQA metric
      :cite:`xue-2013-perceptual`. This implementation is a translation of the
      reference Matlab implementation provided by the authors of
      :cite:`xue-2013-perceptual`.
  
      Parameters
      ----------
      vref : array_like
        Reference image
      vcmp : array_like
        Comparison image
      rescale : bool, optional (default True)
        Rescale inputs so that `vref` has a maximum value of 255, as assumed
        by reference implementation
  
      Returns
      -------
      score : float
        PAMSE IQA metric