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.

翻译：自动感知图像质量评估是一个具有挑战性的问题，每天影响数十亿互联网和社交媒体用户。为推进该领域研究，我们提出了一种混合专家方法，在无监督环境下训练两个独立的编码器，分别学习高层内容特征和低层图像质量特征。该方法的核心创新在于能够生成与表征图像内容的高层特征互补的低层图像质量表征。我们将用于训练这两个编码器的框架称为Re-IQA。针对野外图像质量评估，我们利用从Re-IQA框架中获得的互补低层和高层图像表征来训练线性回归模型，该模型用于将图像表征映射到真实质量分数（见图1）。我们的方法在多个包含真实和合成失真的大规模图像质量评估数据库上取得了最先进的性能，证明了深度神经网络如何在无监督训练下产生感知相关表征。实验结果表明，获得的低层和高层特征确实具有互补性，并对线性回归器的性能产生积极影响。与本工作相关的所有代码将在GitHub上公开发布。