Automatic Perceptual Image Quality Assessment is a challenging problem that
impacts billions of internet, and social media users daily. To advance research
in this field, we propose a Mixture of Experts approach to train two separate
encoders to learn high-level content and low-level image quality features in an
unsupervised setting. The unique novelty of our approach is its ability to
generate low-level representations of image quality that are complementary to
high-level features representing image content. We refer to the framework used
to train the two encoders as Re-IQA. For Image Quality Assessment in the Wild,
we deploy the complementary low and high-level image representations obtained
from the Re-IQA framework to train a linear regression model, which is used to
map the image representations to the ground truth quality scores, refer Figure
1. Our method achieves state-of-the-art performance on multiple large-scale
image quality assessment databases containing both real and synthetic
distortions, demonstrating how deep neural networks can be trained in an
unsupervised setting to produce perceptually relevant representations. We
conclude from our experiments that the low and high-level features obtained are
indeed complementary and positively impact the performance of the linear
regressor. A public release of all the codes associated with this work will be
made available on GitHub.

本文提出了一种混合专家方法来进行自动感知图像质量评估，使用 Re-IQA 框架训练两个独立的编码器来学习高级和低级图像特征，并使用得到的图像表示来训练线性回归模型以提高评估准确性。实验表明，深度神经网络在无监督设置下训练可以产生具有感知相关性的表示。