Towards Explainable Partial-AIGC Image Quality Assessment
Journal:
arXiv
Published Date:
Apr 12, 2025
Abstract
The rapid advancement of AI-driven visual generation technologies has
catalyzed significant breakthroughs in image manipulation, particularly in
achieving photorealistic localized editing effects on natural scene images
(NSIs). Despite extensive research on image quality assessment (IQA) for
AI-generated images (AGIs), most studies focus on fully AI-generated outputs
(e.g., text-to-image generation), leaving the quality assessment of
partial-AIGC images (PAIs)-images with localized AI-driven edits an almost
unprecedented field. Motivated by this gap, we construct the first large-scale
PAI dataset towards explainable partial-AIGC image quality assessment (EPAIQA),
the EPAIQA-15K, which includes 15K images with localized AI manipulation in
different regions and over 300K multi-dimensional human ratings. Based on this,
we leverage large multi-modal models (LMMs) and propose a three-stage model
training paradigm. This paradigm progressively trains the LMM for editing
region grounding, quantitative quality scoring, and quality explanation.
Finally, we develop the EPAIQA series models, which possess explainable quality
feedback capabilities. Our work represents a pioneering effort in the
perceptual IQA field for comprehensive PAI quality assessment.
