Essence of image quality

Perceptual image quality is a complex, multifaceted attribute influenced by factors such as sharpness, noise, contrast, and semantic content. Traditional image quality assessment methods measure overall quality but lack interpretable decompositions to isolate specific perceptual factors. In this work, we leverage the generative capacity of text-to-image diffusion models, which can be leveraged through textual inversion to decompose image quality into disentangled embeddings. By structuring these embeddings according to perceptual quality metrics such as Mean Opinion Score, our framework enables decomposing image quality while preserving semantic content. Moreover, this decomposition provides a principled way to investigate the underlying factors and essence of perceptual image quality, including the role of semantic content. To our knowledge, this is the first framework that decomposes image quality using the generative power of diffusion models.