Abstract No-reference super-resolution (SR) image quality assessment (NR-SRIQA) aims to evaluate the quality of SR images without relying on any reference images. Currently, most previous methods usually utilize a certain handcrafted perceptual statistical features to quantify the degradation of SR images and a simple regression model to learn the mapping relationship from the features to the perceptual quality. Although these methods achieved promising performance, they still have some limitations: 1) the handcrafted features cannot accurately quantify the degradation of SR images; 2) the complex mapping relationship between the features and the quality scores cannot be well approximated by a simple regression model. To alleviate the above problems, we propose a novel stacking regression framework for NR-SRIQA. In the proposed method, we use a pre-trained VGGNet to extract the deep features for measuring the degradation of SR images, and then develop a stacking regression framework to establish the relationship between the learned deep features and the quality scores to achieve the NR-SRIQA. The stacking regression integrates two base regressors, namely Support Vector Regression (SVR) and K-Nearest Neighbor (K-NN) regression, and a simple linear regression as a meta-regressor. Thanks to the feature representation capability of deep neural networks (DNNs) and the complementary features of the two base regressors, the experimental results indicate that the proposed stacking regression framework is capable of yielding higher consistency with human visual judgments on the quality of SR images than other state-of-the-art SRIQA methods.

中文翻译：

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无参考超分辨率图像质量评估的学习堆叠回归

摘要 无参考超分辨率 (SR) 图像质量评估 (NR-SRIQA) 旨在在不依赖任何参考图像的情况下评估 SR 图像的质量。目前，大多数以前的方法通常利用某种手工制作的感知统计特征来量化 SR 图像的退化和一个简单的回归模型来学习从特征到感知质量的映射关系。尽管这些方法取得了不错的性能，但它们仍然存在一些局限性：1）手工制作的特征无法准确量化 SR 图像的退化；2）特征与质量分数之间复杂的映射关系不能用简单的回归模型很好地近似。为了缓解上述问题，我们为 NR-SRIQA 提出了一种新的堆叠回归框架。在提出的方法中，我们使用预训练的 VGGNet 提取深度特征来衡量 SR 图像的退化，然后开发堆叠回归框架来建立学习的深度特征与质量分数之间的关系，以实现 NR-SRIQA。堆叠回归集成了两个基本回归量，即支持向量回归 (SVR) 和 K-最近邻 (K-NN) 回归，以及作为元回归量的简单线性回归。由于深度神经网络 (DNN) 的特征表示能力和两个基本回归器的互补特征，实验结果表明，与人类对 SR 图像质量的视觉判断相比，所提出的堆叠回归框架能够产生更高的一致性。其他最先进的 SRIQA 方法。