This paper discusses the challenges of evaluating deblurring-methods quality and proposes a reduced-reference metric based on machine learning. Traditional quality-assessment metrics such as PSNR and SSIM are common for this task, but not only do they correlate poorly with subjective assessments, they also require ground-truth (GT) frames, which can be difficult to obtain in the case of deblurring. To develop and evaluate our metric, we created a new motion-blur dataset using a beam splitter. The setup captured various motion types using a static camera, as most scenes in existing datasets include blur due to camera motion. We also conducted two large subjective comparisons to aid in metric development. Our resulting metric requires no GT frames, and it correlates well with subjective human perception of blur.

翻译：本文讨论了评估去模糊方法质量的挑战，并提出了一种基于机器学习的降参考度量。传统的质量评估指标（如PSNR和SSIM）虽常用于此任务，但它们不仅与主观评估的相关性较差，而且需要真实帧，这在去模糊情况下难以获取。为了开发和评估我们的度量标准，我们使用分束器创建了一个新的运动模糊数据集。该设置使用静态相机捕捉了多种运动类型，因为现有数据集中的大多数场景包含由相机运动引起的模糊。我们还进行了两次大规模主观比较以辅助度量开发。最终得到的度量标准无需真实帧，且与人类对模糊的主观感知具有良好相关性。