Currently, most image quality assessment (IQA) models are supervised by the
MAE or MSE loss with empirically slow convergence. It is well-known that
normalization can facilitate fast convergence. Therefore, we explore
normalization in the design of loss functions for IQA. Specifically, we first
normalize the predicted quality scores and the corresponding subjective quality
scores. Then, the loss is defined based on the norm of the differences between
these normalized values. The resulting "Norm-in-Norm'' loss encourages the IQA
model to make linear predictions with respect to subjective quality scores.
After training, the least squares regression is applied to determine the linear
mapping from the predicted quality to the subjective quality. It is shown that
the new loss is closely connected with two common IQA performance criteria
(PLCC and RMSE). Through theoretical analysis, it is proved that the embedded
normalization makes the gradients of the loss function more stable and more
predictable, which is conducive to the faster convergence of the IQA model.
Furthermore, to experimentally verify the effectiveness of the proposed loss,
it is applied to solve a challenging problem: quality assessment of in-the-wild
images. Experiments on two relevant datasets (KonIQ-10k and CLIVE) show that,
compared to MAE or MSE loss, the new loss enables the IQA model to converge
about 10 times faster and the final model achieves better performance. The
proposed model also achieves state-of-the-art prediction performance on this
challenging problem. For reproducible scientific research, our code is publicly
available at this https URL

通过对图像质量评估中的损失函数进行规范化处理，设计出了 “规范内规范” 损失函数，可以更加稳定和快速地进行目标预测和更好地评估野外图像的质量，并在相关数据集上展现了明显的优势。