A Machine Learning based Reduced-Reference Image Quality Assessment Method for Single-Image Super-Resolution
Liangkang Yang, Yuxia Sheng, Li Chai · 2019
The existing image quality assessment methods can be classified into full-reference, no-reference, and reduced-reference methods. However, full-reference methods require ground-truth images which are unavailable for single-image super-resolution (SISR). No-reference methods do not require reference images, but establishing a model is more complicated. Low-resolution (LR) images are known for SISR, and partial information can be provided as reference. In this paper, we propose a machine learning based reduced-reference image assessment method for SISR. LR images are used as reference images. First, we extract the statistical features of LR images and super-resolution (SR) images in spatial domain and log-Gabor filter response respectively. Then, learn a two-stage regression model to measure the quality of SR images referring to LR images. Compared with the state-of-art methods, experimental results proved that the proposed method is more consistent with human visual perception.