Q-Norm

[ICCV'25] Q-Norm

Getting Started

(1) Get image quality feature

Refer to the official documentation of CONTRIQUE here and download the weights

You can also use other quality representations by other advanced BIQA models, but you need to modify the feature dimensions to make them match.

from CONTRIQUE_model import CONTRIQUE_model
from torchvision import transforms
import torch

b, c, h, w = x.shape
image_2 = transforms.Resize([h // 2, w // 2])(x)
quality_model = CONTRIQUE_model(models.resnet50(pretrained=False), 2048)
quality_model.load_state_dict(torch.load('CONTRIQUE_checkpoint25.tar'))
device=x.device
quality_model = quality_model.to(device)
quality_model.eval()
_, _, _, _, model_feat, model_feat_2, _, _ = quality_model(x, image_2)
quality = torch.hstack((model_feat, model_feat_2))

(2) Integrate Q-Norm into the model

from qnorm import QualityNorm
qn=QualityNorm(num_features=in_channel)