Technical report on visual quality assessment for frame interpolation

Hui Men, Hanhe Lin, Vlad Hosu, Daniel Maurer, Andrés Bruhn, Dietmar Saupe

Research output: Working paper/PreprintPreprint

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Current benchmarks for optical flow algorithms evaluate the estimation quality by comparing their predicted flow field with the ground truth, and additionally may compare interpolated frames, based on these predictions, with the correct frames from the actual image sequences. For the latter comparisons, objective measures such as mean square errors are applied. However, for applications like image interpolation, the expected user's quality of experience cannot be fully deduced from such simple quality measures. Therefore, we conducted a subjective quality assessment study by crowdsourcing for the interpolated images provided in one of the optical flow benchmarks, the Middlebury benchmark. We used paired comparisons with forced choice and reconstructed absolute quality scale values according to Thurstone's model using the classical least squares method. The results give rise to a re-ranking of 141 participating algorithms w.r.t. visual quality of interpolated frames mostly based on optical flow estimation. Our re-ranking result shows the necessity of visual quality assessment as another evaluation metric for optical flow and frame interpolation benchmarks.
Original languageEnglish
Place of PublicationCornell University
Number of pages11
Publication statusPublished - 26 Mar 2019


  • visual quality assessment
  • optical flow
  • frame interpolation


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