Large Models No Longer "Guess" Image Scores, Use "Visual Evidence" like Structural Maps and Spectrograms as "Physical Evidence" to Score Images
Multimodal large language models (MLLMs) often perform poorly on image quality assessment (IQA), as they primarily rely on semantic understanding and are insensitive to underlying degradations like noise or blur. To address this, researchers from Northwestern Polytechnical University and Hong Kong University of Science and Technology introduced IQA-T1, a novel framework that enables MLLMs to generate and use structured "visual evidence"—such as noise residual maps, Fourier magnitude spectra, and gradient orientation coherence maps—for reasoning. IQA-T1 actively selects tools from a dedicated perceptual library to produce this evidence, shifting from intuitive "guesswork" to evidence-based assessment. Trained via supervised fine-tuning and reinforcement learning on a newly created Q-Tool dataset, the model learns to call tools efficiently. Evaluated across seven IQA benchmarks, IQA-T1 achieves state-of-the-art average PLCC/SRCC scores of 0.795/0.784 while providing explainable, traceable reasoning chains.
marsbit07/20 07:47
