SageIQ: Scene-Graph-Guided Blind Image Quality Assessment

Abstract

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.