pyiqa.archs.piqe_arch
=====================

.. py:module:: pyiqa.archs.piqe_arch

.. autoapi-nested-parse::

   PIQE metric implementation.

   Paper:
       N. Venkatanath, D. Praneeth, Bh. M. Chandrasekhar, S. S. Channappayya, and S. S. Medasani. "Blind Image Quality Evaluation Using Perception Based Features", In Proceedings of the 21st National Conference on Communications (NCC). Piscataway, NJ: IEEE, 2015.

   .. rubric:: References

   - Matlab: https://www.mathworks.com/help/images/ref/piqe.html
   - Python: https://github.com/michael-rutherford/pypiqe

   This PyTorch implementation by: Chaofeng Chen (https://github.com/chaofengc)







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

.. py:function:: piqe(img: torch.Tensor, block_size: int = 16, activity_threshold: float = 0.1, block_impaired_threshold: float = 0.1, window_size: int = 6) -> torch.Tensor

   Calculates the Perceptual Image Quality Estimator (PIQE) score for an input image.
   :param - img: The input image tensor.
   :type - img: torch.Tensor
   :param - block_size: The size of the blocks used for processing. Defaults to 16.
   :type - block_size: int, optional
   :param - activity_threshold: The threshold for considering a block as active. Defaults to 0.1.
   :type - activity_threshold: float, optional
   :param - block_impaired_threshold: The threshold for considering a block as impaired. Defaults to 0.1.
   :type - block_impaired_threshold: float, optional
   :param - window_size: The size of the window used for block analysis. Defaults to 6.
   :type - window_size: int, optional

   :returns: The PIQE score for the input image.
   :rtype: - torch.Tensor


.. py:function:: noise_criterion(block, block_size, block_var)

   Function to analyze block for Gaussian noise distortions.


.. py:function:: cal_center_sur_dev(block, block_size)

   Function to compute center surround Deviation of a block.


.. py:function:: notice_dist_criterion(blocks, window_size, block_impaired_threshold, N)

   Analyze blocks for noticeable artifacts and Gaussian noise distortions.

   :param blocks: Tensor of shape (b, num_blocks, block_size, block_size).
   :type blocks: torch.Tensor
   :param window_size: Size of the window for segment analysis.
   :type window_size: int
   :param block_impaired_threshold: Threshold for considering a block as impaired.
   :type block_impaired_threshold: float
   :param N: Size of the blocks (same as block_size).
   :type N: int

   :returns: Tensor indicating impaired blocks.
   :rtype: torch.Tensor


.. py:class:: PIQE

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


   PIQE module.

   :param x: Input tensor of shape (B, C, H, W).
   :type x: torch.Tensor

   :returns: PIQE score.
   :rtype: torch.Tensor


   .. py:method:: get_masks()


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