Super-Resolution Quality Assessment: Subjective Evaluation Database and Quality Index Based on Perceptual Structure Measurement

• Main Authors: Wan, Wenfei, Wu, Jinjian, Shi, Guangming, Li, Yongbo, Dong, Weisheng
• Format: Conference Proceeding
• Language: English
• Subjects: human visual system (HVS); image quality assessment (IQA); perceptual structure measurement; Single image super-resolution (SISR)
• ISSN: 1945-788X
• DOI: 10.1109/ICME.2018.8486519

With the outstanding performance of deep learning based single image super-resolution (SISR) methods, the traditional SISR evaluation metrics (e.g., PSNR and SSIM, which measure the per-pixel differences and simple structure similarities respectively) are facing great challenges. When assessing SISR algorithms, they generally are hardly consistent with the human visual system (HVS). According to the psychological studies, the HVS presents different sensitivities to the plain, edge and texture regions, which are difficult to be accurately identified and measured with the existing quality indexes, especially for SR images. To deal with this problem, we firstly build a SISR subjective assessment database including several major deep learning based SR methods. Then we propose a more accurate perception structure measurement and use their similarity comparisons to evaluate the SR algorithms. Experimental results on the databases demonstrate that the proposed method performs well consistent with the human visual perception.