Abstract
While slow motion has become a standard feature in mainstream cell phones, a fast approach without relying on specific training datasets to assess slow motion video quality is not available. Conventionally, researchers evaluate their algorithms with peak signal-to-noise ratio (PSNR) or structural similarity index measure (SSIM) between ground-truth and reconstructed frames. But they are both global evaluation index and more sensitive to noise or distortion brought by the interpolation. For video interpolation, especially for fast moving objects, motion blur as well as ghost problem are more essential to the audience subjective judgment. How to achieve a proper evaluation for Video Frame Interpolation (VFI) task is still a problem that is not well addressed.
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