Learning stacking regression for no-reference super-resolution image quality assessment

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.