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，影响着全球数十亿互联网和社交媒体用户的日常体验。为推进该领域研究，我们提出一种混合专家方法，在无监督环境下训练两个独立的编码器，分别学习高层级的内容特征和低层级的图像质量特征。该方法的核心创新在于能够生成与表征图像内容的高层级特征互补的低层级图像质量表征。我们将用于训练这两个编码器的框架称为Re-IQA。在真实场景图像质量评估中，我们利用Re-IQA框架获得的互补性低层级和高层级图像表征，训练一个线性回归模型，用于将图像表征映射到真实质量分数（参见图1）。我们的方法在多个包含真实与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环境下进行训练以生成感知相关表征。实验结果表明，所获得的低层级与高层级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。