pyiqa.archs.unique_arch

LIQE Model

github repo link: https://github.com/zwx8981/UNIQUE

Cite as: @article{zhang2021uncertainty,

title = {Uncertainty-aware blind image quality assessment in the laboratory and wild}, author = {Zhang, Weixia and Ma, Kede and Zhai, Guangtao and Yang, Xiaokang}, journal = {IEEE Transactions on Image Processing}, volume = {30}, pages = {3474–3486}, month = {Mar.}, year = {2021}

}

Module Contents

pyiqa.archs.unique_arch.default_model_urls

class pyiqa.archs.unique_arch.Normalize(mean, std)

Bases: torch.nn.Module

forward(x)

class pyiqa.archs.unique_arch.BCNN(thresh=1e-08, is_vec=True, input_dim=512)

Bases: torch.nn.Module

forward(x)

class pyiqa.archs.unique_arch.UNIQUE

Bases: torch.nn.Module

Full UNIQUE network. :param - default_mean: Default mean value. :type - default_mean: list :param - default_std: Default std value. :type - default_std: list

forward(x)

Compute IQA using UNIQUE model.

Parameters:

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

Returns:

Value of UNIQUE model.