ME-IQA: Memory-Enhanced Image Quality Assessment via Re-Ranking
Kanglong Fan, Tianhe Wu, Wen Wen, Jianzhao Liu, Le Yang, Yabin Zhang, Yiting Liao, Junlin Li, Li Zhang

TL;DR
ME-IQA enhances image quality assessment by integrating memory and re-ranking to produce more sensitive and continuous scores, overcoming the limitations of scalar score collapse in reasoning-based vision-language models.
Contribution
It introduces a novel memory-enhanced re-ranking framework that improves the sensitivity and continuity of IQA scores in reasoning-induced VLMs.
Findings
Consistent performance improvements across multiple IQA benchmarks.
Mitigation of discrete score collapse in reasoning-based models.
Effective use of memory and re-ranking for better quality assessment.
Abstract
Reasoning-induced vision-language models (VLMs) advance image quality assessment (IQA) with textual reasoning, yet their scalar scores often lack sensitivity and collapse to a few values, so-called discrete collapse. We introduce ME-IQA, a plug-and-play, test-time memory-enhanced re-ranking framework. It (i) builds a memory bank and retrieves semantically and perceptually aligned neighbors using reasoning summaries, (ii) reframes the VLM as a probabilistic comparator to obtain pairwise preference probabilities and fuse this ordinal evidence with the initial score under Thurstone's Case V model, and (iii) performs gated reflection and consolidates memory to improve future decisions. This yields denser, distortion-sensitive predictions and mitigates discrete collapse. Experiments across multiple IQA benchmarks show consistent gains over strong reasoning-induced VLM baselines, existing…
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Taxonomy
TopicsImage and Video Quality Assessment · Visual Attention and Saliency Detection · Image Enhancement Techniques
