用合成失真数据实现图像质量的局部化、方向性评估
Pixel Perfect: Relational Image Quality Assessment with Spatially-Aware Distortions
- 通过自监督生成失真数据，无需人工标注
- 输出失真类型、强度与相对方向的空间图谱
- 适合需要精准优化图像处理算法的场景
传统图像质量评估（IQA）依赖主观评分（MOS），成本高且无法提供具体失真的定位反馈。本文将质量评估从绝对预测转向关系化、方向性判断。提出自监督合成失真引擎生成训练数据，避免人工标注。训练一个失真预测网络，采用反对称目标函数，生成空间感知、解耦的失真图，可识别相对于参考图像的失真类型、强度与方向。随后，基于有序图像集的对比学习训练得分网络，预测关系性质量分。该方法无需人类标注质量分数，可为图像处理算法的精细化优化提供更细粒度、可解释的评估支持。
原文摘要 · Abstract (English)
Traditional image quality assessment (IQA) methods rely on mean opinion scores (MOS), which are resource-intensive to collect and fail to provide interpretable, localized feedback on specific image distortions. We overcome these limitations by shifting from absolute quality prediction to a relational and directional assessment. Our approach utilizes a self-supervised synthetic distortion engine to generate training data, eliminating the need for manual annotation. A distortion prediction network is trained with an anti-symmetric objective to produce spatially-aware, disentangled maps that identify the type, intensity, and direction of distortions relative to a reference image. Subsequently, a scoring network is trained via contrastive learning on ordinally ranked image sets to predict a relational quality score. Our method provides a more granular and interpretable approach to IQA for the targeted optimization of image processing algorithms without requiring any human-labeled quality scores.
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