Learning a Cascade Regression for No-Reference Super-Resolution Image Quality Assessment
Kaibing Zhang, Danni Zhu, Junfeng Jing, Xinbo Gao · 2019
No-reference super-resolution image quality assessment (NRSRIQA) technique has been recognized an effective way to evaluate the quality of SR images and the performance of SR algorithms. In this paper, we propose a novel NR-SRIQA method by learning a two-layer regression model to establish the mapping relationship between the multiple natural statistical features and visual perceptual scores. First, we exploit three types of statistical features to quantify the degradation of SR images. Next, a cascade two-layer regression model, which integrates AdaBoost Decision Tree Regression and ridge regression, is trained to predict the quality of SR images in a coarse-to-fine manner. The experimental results demonstrate that the proposed method is superior to other previous SR quality evaluation approaches and shows better consistency with visual perception quality.