ME-IQA: Memory-Enhanced Image Quality Assessment via Re-Ranking

Reasoning-induced vision-language models (VLMs) advanceimage quality assessment (IQA) with textual reasoning, yet their scalarscores often lack sensitivity and collapse to a few values, so-called discretecollapse. We introduce ME-IQA, a plug-and-play, test-time memory-enhanced re-ranking framework. It (i) builds a memory bank and re-trieves semantically and perceptually aligned neighbors using reason-ing summaries, (ii) reframes the VLM as a probabilistic comparator toobtain pairwise preference probabilities and fuse this ordinal evidencewith the initial score under Thurstone’s Case V model, and (iii) per-forms gated reflection and consolidates memory to improve future deci-sions. This yields denser, distortion-sensitive predictions and mitigatesdiscrete collapse. Experiments across multiple IQA benchmarks showconsistent gains over strong reasoning-induced VLM baselines, existingnon-reasoning IQA methods, and test-time scaling alternatives.