Data for: SISRSet: Single Image Super-Resolution Subjective Evaluation Test and Objective Quality Assessment

Published: 31 March 2020| Version 1 | DOI: 10.17632/dsnppntnp6.1
jinjian wu


This SISRSet database is established for single image super-resolution quality assessment study. For the subjective evaluation tests, there are 15 pictures chosen from Set5, Set14 and BSD100 as the ground-truth images. The corresponding LR images are obtained by Bicubic method with downscaling factors of 2, 3 and 4. There are 360 SR images generated by 8 SR algorithms with three scaling factors in total. The 8 SR algorithms include the traditional methods: Bicubic, A+, ANR, SelfExSR and the deep learning based SR methods: CSCN, SRCNN, DRCN, VDSR. We chosed the pairwise comparison method to conduct the subjective evaluation test. There are 16 participants without knowledge of the ground-truth and SR images. The setting of the viewing environment and the test condition follow the ITU-R BT.500-11 standard. All original images and the SR images are in the file package. Their MOS values and deviation are also included. The codes of several IQA metrics are introduced in the file. Meanwhile, some experimental results are shown in the file.



Computer Vision, Super-Resolution Imaging, Image Quality