KSIQA: A Knowledge-Sharing Model for No-Reference Image Quality Assessment

This paper presents KSIQA, a new machine learning model that measures image quality without needing a reference image to compare against (no-reference image quality assessment, or NR-IQA). The model uses a knowledge-sharing strategy where a teacher model that can see reference images helps train a student model to predict quality by generating mental imagery and combining different types of feature extraction (vision transformers, which break images into patches for analysis, and convolutional neural networks, which apply filters to detect patterns). The researchers show their model performs better than existing no-reference quality assessment methods on standard test datasets.