提出双表示交互机制，提升图像质量评估在真实退化场景下的性能。
Dual-Representation Interaction Driven Image Quality Assessment with Restoration Assistance
- 分离建模退化与质量信息，通过双表示交互增强表征能力
- 在合成与真实数据集上均优于现有最先进模型，尤其对修复图像有效
- 适合需要高鲁棒性图像质量评估的研究者与工业应用
无参考图像质量评估因图像内容差异和退化类型多样而始终具有挑战性。以往的IQA模型多基于合成图像显式编码单一质量特征，但在面对真实世界退化或修复模型生成的图像时性能下降，原因在于未充分考虑低质量图像的退化因素。为此，本文提出DRI方法，分别获取图像的退化向量与质量向量，以独立建模退化与质量信息。随后引入修复网络，为MOS评分预测器提供退化信息。进一步设计基于表征的语义损失（RS Loss），促进表征间的有效交互。大量实验表明，该方法在合成与真实世界数据集上均优于现有最先进模型。
原文摘要 · Abstract (English)
No-Reference Image Quality Assessment for distorted images has always been a challenging problem due to image content variance and distortion diversity. Previous IQA models mostly encode explicit single-quality features of synthetic images to obtain quality-aware representations for quality score prediction. However, performance decreases when facing real-world distortion and restored images from restoration models. The reason is that they do not consider the degradation factors of the low-quality images adequately. To address this issue, we first introduce the DRI method to obtain degradation vectors and quality vectors of images, which separately model the degradation and quality information of low-quality images. After that, we add the restoration network to provide the MOS score predictor with degradation information. Then, we design the Representation-based Semantic Loss (RS Loss) to assist in enhancing effective interaction between representations. Extensive experimental results demonstrate that the proposed method performs favorably against existing state-of-the-art models on both synthetic and real-world datasets.
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