In this paper, we address the well-known image quality assessment problem but in contrast from existing approaches that predict image quality independently for every images, we propose to jointly model different images depicting the same content to improve the precision of quality estimation. This proposal is motivated by the idea that multiple distorted images can provide information to disambiguate image features related to content and quality. To this aim, we combine the feature representations from the different images to estimate a pseudo-reference that we use to enhance score prediction. Our experiments show that at test-time, our method successfully combines the features from multiple images depicting the same new content, improving estimation quality.

翻译：本文针对图像质量评估这一经典问题，提出与现有独立预测每张图像质量的方法不同的新思路：通过联合建模同一内容的不同图像，提升质量估计的精度。这一方法的出发点在于，多幅失真图像可为区分图像特征中内容与质量相关成分提供信息。为此，我们整合不同图像的特征表示，估计出一个伪参考（pseudo-reference），并利用该参考增强分数预测。实验结果表明，在测试阶段，该方法能有效整合描绘同一新内容的多幅图像特征，从而改善质量估计效果。