In this work, we introduce Gradient Siamese Network (GSN) for image quality assessment. The proposed method is skilled in capturing the gradient features between distorted images and reference images in full-reference image quality assessment(IQA) task. We utilize Central Differential Convolution to obtain both semantic features and detail difference hidden in image pair. Furthermore, spatial attention guides the network to concentrate on regions related to image detail. For the low-level, mid-level and high-level features extracted by the network, we innovatively design a multi-level fusion method to improve the efficiency of feature utilization. In addition to the common mean square error supervision, we further consider the relative distance among batch samples and successfully apply KL divergence loss to the image quality assessment task. We experimented the proposed algorithm GSN on several publicly available datasets and proved its superior performance. Our network won the second place in NTIRE 2022 Perceptual Image Quality Assessment Challenge track 1 Full-Reference.

本文介绍了一种名为Gradient Siamese Network的图像质量评估方法，使用中心微分卷积获取图像对之间的语义特征和细节差异并通过空间注意力引导网络集中在与图像细节相关的区域，通过多级融合方法改善了特征利用效率，并成功地将KL散度损失应用于图像质量评估任务。该算法在NTIRE 2022感知图像质量评估挑战的全参考轨道1中获得了第二名。

梯度孪生网络进行图像质量评估