video super-resolution quality assessment database (VSR-QAD)-1

Video super-resolution (SR) has important real world applications such as enhancing viewing experiences of legacy low-resolution videos on high resolution display devices. However, there are no visual quality assessment (VQA) models specifically designed for evaluating SR videos while such models are crucially important both for advancing video SR algorithms and for viewing quality assurance. Therefore, we establish a super-resolution video quality assessment database （VSR-QAD） for implementing super-resolution video quality assessment. Our VSR-QAD consists of 120 high quality high resolution reference videos. These videos were first spatially down-scaled by a factor of x2, x4, and x8, and then super-resolved back to their original resolutions by 10 representative SR algorithms to obtain 2,400 SR videos. Psychovisual experiments were carried out to acquire subjective quality labels for these SR videos. 190 subjects spending a total of approximately 400 hours to carry out both absolute rating and relative rating experiments. After aligning these relative and absolute scores and removing outliers, 2,260 SR videos are labeled with mean opinion scores (MOSs).

视频超分辨率（SR）技术在现实世界中具有至关重要的应用价值，例如提升老旧低分辨率视频在高分辨率显示设备上的观看体验。然而，目前尚无专门针对评估超分辨率视频的视觉质量评估（VQA）模型，而此类模型对于推进视频超分辨率算法的发展以及确保观看质量至关重要。因此，本研究构建了一个超分辨率视频质量评估数据库（VSR-QAD），旨在实施超分辨率视频质量评估。本VSR-QAD包含120部高质量高分辨率参考视频。这些视频首先经过x2、x4和x8的倍数空间降采样，随后由10种代表性的超分辨率算法将它们恢复至原始分辨率，从而获得2,400部超分辨率视频。为了获取这些超分辨率视频的主观质量标签，我们进行了心理视觉实验。190名受试者投入了总计约400小时的时间，进行了绝对评分和相对评分实验。在将相对和绝对评分进行对齐并剔除异常值后，共2,260部超分辨率视频被标注了平均意见评分（MOSs）。