pyiqa.archs.deepdc_arch
=======================

.. py:module:: pyiqa.archs.deepdc_arch

.. autoapi-nested-parse::

   DeepDC: Deep Distance Correlation as a Perceptual Image Quality Evaluator

   Reference:
   @article{zhu2024adaptive,
     title={DeepDC: Deep Distance Correlation as a Perceptual Image Quality Evaluator},
     author={Zhu, Hanwei and Chen, Baoliang and Zhu, Lingyu and Wang, Shiqi and Weisi, Lin},
     journal={arXiv preprint arXiv:2211.04927},
     year={2024},
   }

   Reference url: https://github.com/h4nwei/DeepDC









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

.. py:data:: names

.. py:class:: MultiVGGFeaturesExtractor(target_features=('conv1_2', 'conv2_2', 'conv3_4', 'conv4_4', 'conv5_4'), use_input_norm=True, requires_grad=False)

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


   .. py:method:: forward(x)


.. py:class:: DeepDC(features_to_compute=('conv1_2', 'conv2_2', 'conv3_4', 'conv4_4', 'conv5_4'))

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


   .. py:method:: forward(x, y)

      Compute IQA using DeepDC model.

      :param - x: An input tensor with (N, C, H, W) shape. RGB channel order for colour images.
      :param - y: An reference tensor with (N, C, H, W) shape. RGB channel order for colour images.

      :returns: Value of DeepDC model.



   .. py:method:: Distance_Correlation(matrix_A, matrix_B)


.. py:function:: prepare_image(image, resize=True)

.. py:data:: parser