pyiqa.archs.deepdc_arch

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

pyiqa.archs.deepdc_arch.names

class pyiqa.archs.deepdc_arch.MultiVGGFeaturesExtractor(target_features=('conv1_2', 'conv2_2', 'conv3_4', 'conv4_4', 'conv5_4'), use_input_norm=True, requires_grad=False)

Bases: torch.nn.Module

forward(x)

class pyiqa.archs.deepdc_arch.DeepDC(features_to_compute=('conv1_2', 'conv2_2', 'conv3_4', 'conv4_4', 'conv5_4'))

Bases: torch.nn.Module

forward(x, y)

Compute IQA using DeepDC model.

Parameters:

• x (-) – An input tensor with (N, C, H, W) shape. RGB channel order for colour images.

• y (-) – An reference tensor with (N, C, H, W) shape. RGB channel order for colour images.

Returns:

Value of DeepDC model.

Distance_Correlation(matrix_A, matrix_B)

pyiqa.archs.deepdc_arch.prepare_image(image, resize=True)

pyiqa.archs.deepdc_arch.parser