FoMo: Forking Moment in Generative Trajectory as a Perceptual Distance

Reference-based image quality assessment (IQA) metrics aim to reflect how humans perceive the perceptual distance between a pair of images. To learn how the human visual system (HVS) operates, recent reference-based IQA metrics heavily rely on human-annotated data. Mean opinion score (MOS)-based pointwise scoring, which assigns a scalar quality value per image, is preferable fo

This paper introduces a novel, automated approach for generating perceptual distance data, which is crucial for training and evaluating image quality assessment (IQA) models. For ML engineers, this means more efficient and scalable ways to obtain high-quality training data for computer vision tasks, reducing reliance on expensive and noisy human annotations.