SageIQ: Scene-Graph-Guided Blind Image Quality Assessment

Conventional Blind Image Quality Assessment (BIQA) methods typically assess the entire image quality, which is suboptimal for tasks like autonomous driving that concern specific Task-Aligned Region (TAR). Moreover, we observe that advanced Multimodal Large Language Model (MLLM)-based BIQA models exhibit bias when evaluating small regions, leading to inaccurate perceptual judgments. To address these issues, we propose SageIQ (Scene-graph-guided Evaluation for Image Quality), a pipeline SageIQ-P for TAR localization, an approach consisting of an MLLM-based BIQA model SageIQ-M, and a dataset SageIQ-D. SageIQ-P is designed to automatically identify and evaluate TARs, with the advantages of being training-free and allowing plug-in integration of off-the-shelf BIQA models without retraining. It operates in three stages: scenegraphbased triplet construction, LLMdriven triplet analysis, and integration of weighted BIQA scores into a final assessment. Since SageIQ-P can produce small-sized TAR crops that may encounter small-region scoring bias in existing BIQA models, we propose SageIQ-M to alleviate this bias by injecting scale information through scale-aware images and size-prompted cues, achieving size awareness across both visual and textual modalities. In addition, we develop a fully automated approach to construct a region-level test set SageIQ-D, significantly reducing the human effort needed. Experimental results demonstrate that our methods achieve superior BIQA performance.