2 research outputs found
Norm-in-Norm Loss with Faster Convergence and Better Performance for Image Quality Assessment
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 https://github.com/lidq92/LinearityIQA.Comment: Accepted by ACM MM 2020, + supplemental material