pyiqa.archs.gmsd_arch
=====================

.. py:module:: pyiqa.archs.gmsd_arch

.. autoapi-nested-parse::

   GMSD Metric

   @article{xue2013gmsd,
     title={Gradient magnitude similarity deviation: A highly efficient perceptual image quality index},
     author={Xue, Wufeng and Zhang, Lei and Mou, Xuanqin and Bovik, Alan C},
     journal={IEEE transactions on image processing},
     volume={23},
     number={2},
     pages={684--695},
     year={2013},
     publisher={IEEE}
   }

   Created by: https://github.com/dingkeyan93/IQA-optimization/blob/master/IQA_pytorch/GMSD.py

   Modified by: Jiadi Mo (https://github.com/JiadiMo)

   Refer to:
       Matlab code from https://www4.comp.polyu.edu.hk/~cslzhang/IQA/GMSD/GMSD.m;







Module Contents
---------------

.. py:function:: gmsd(x: torch.Tensor, y: torch.Tensor, T: int = 170, channels: int = 3, test_y_channel: bool = True) -> torch.Tensor

   GMSD metric.
   :param - x: A distortion tensor. Shape :math:`(N, C, H, W)`.
   :param - y: A reference tensor. Shape :math:`(N, C, H, W)`.
   :param - T: A positive constant that supplies numerical stability.
   :param - channels: Number of channels.
   :param - test_y_channel: bool, whether to use y channel on ycbcr.


.. py:class:: GMSD(channels: int = 3, test_y_channel: bool = True)

   Bases: :py:obj:`torch.nn.Module`


   Gradient Magnitude Similarity Deviation Metric.
   :param - channels: Number of channels.
   :param - test_y_channel: bool, whether to use y channel on ycbcr.

   Reference:
       Xue, Wufeng, Lei Zhang, Xuanqin Mou, and Alan C. Bovik.
       "Gradient magnitude similarity deviation: A highly efficient
       perceptual image quality index." IEEE Transactions on Image
       Processing 23, no. 2 (2013): 684-695.


   .. py:method:: forward(x: torch.Tensor, y: torch.Tensor) -> torch.Tensor

      Args:
      x: A distortion tensor. Shape :math:`(N, C, H, W)`.
      y: A reference tensor. Shape :math:`(N, C, H, W)`.
      Order of input is important.