Perceptual evaluation of single-image super-resolution reconstruction

Abstract

In recent years, single-image super-resolution (SR) reconstruction has aroused wide attention. Massive SR enhancement algorithms have been proposed. However, much less work has been down on the perceptual evaluation of SR enhanced images and the corresponding enhancement algorithms. In this work, we create a Super-resolution Reconstructed Image Database (SRID), which consists of images produced by two interpolation methods and six popular SR image enhancement algorithms at different amplification factors. Then, subjective experiment is conducted to collect the subjective scores by using the single-stimulus method. The performances of the SR image enhancement algorithms are then evaluated by the obtained subjective scores. Finally, the performances of the general-purpose no-reference (NR) image quality metrics are investigated on the SRID database. This study shows that it is difficult for the state-of-the-art NR image quality metrics to predict the quality of SR enhanced images.