arXiv cs.AI· Chuanzhi Xu, Chengkun Yue, Haolan Guo, Xuanhua Yin, Boyu Wei, Zihan Deng, Qiang Qu, Weidong Cai·· 5 小时前AI 评分36
MetaRefine：基于多层级特征的评分细化图像质量评估方法
MetaRefine: Score Refinement with Multi-Level Features for Image Assessment
AI 导读
MetaRefine 是一种元数据感知的评分细化架构，通过轻量残差头结合原始图像几何信息、多层级视觉特征与冻结模型的原始评分来预测分数修正，无需改动原有评分流程。
正文 · 原文
Abstract:An image's original dimensions, aspect ratio, and composition provide important cues that can affect its technical and aesthetic quality. Image assessment methods use resizing, cropping, padding, or patch sampling to accommodate different input sizes. However, these operations can alter or obscure original geometric and compositional cues. Existing assessment models also encode rich multi-level visual evidence that can be further extracted and exploited to improve existing models. This motivates us to combine visual evidence with original-image geometry for score refinement without changing the original scoring pipelines. In this paper, we introduce MetaRefine, a metadata-aware score refinement architecture whose lightweight residual heads predict score corrections from geometry, multi-level visual features, and the frozen model's original score. Across 100 configurations on 21 datasets, MetaRefine improves PLCC in 88 configurations and SRCC in 85, with average relative gains of 7.55% and 7.10%, respectively. It reduces MAE in 90 configurations and RMSE in 89, with average relative reductions of 23.27% and 21.93%, respectively. The median parameter overhead is only 1.11%. MetaRefine can further improve state-of-the-art (SOTA) assessment models on multiple benchmarks.
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.40487 [cs.CV] |
| (or arXiv:2610.40487v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2610.40487 arXiv-issued DOI via DataCite |
Submission history
From: Chuanzhi Xu [view email]
[v1]
Thu, 29 Oct 2026 17:15:30 UTC (3,503 KB)
来源：arXiv cs.AI · arxiv.org