Re-IQA: Unsupervised Learning for Image Quality Assessment in the Wild
This addresses the challenge of image quality assessment for billions of internet and social media users, though it is incremental as it builds on existing unsupervised learning methods.
The paper tackles the problem of automatic perceptual image quality assessment (IQA) by proposing an unsupervised Mixture of Experts approach called Re-IQA, which learns complementary low-level quality and high-level content features, achieving state-of-the-art performance on multiple large-scale IQA databases with real and synthetic distortions.
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.