  Current benchmarks for optical flow algorithms evaluate the estimation either
directly by comparing the predicted flow fields with the ground truth or
indirectly by using the predicted flow fields for frame interpolation and then
comparing the interpolated frames with the actual frames. In the latter case,
objective quality measures such as the mean squared error are typically
employed. However, it is well known that for image quality assessment, the
actual quality experienced by the user cannot be fully deduced from such simple
measures. Hence, we conducted a subjective quality assessment crowdscouring
study for the interpolated frames provided by one of the optical flow
benchmarks, the Middlebury benchmark. We collected forced-choice paired
comparisons between interpolated images and corresponding ground truth. To
increase the sensitivity of observers when judging minute difference in paired
comparisons we introduced a new method to the field of full-reference quality
assessment, called artefact amplification. From the crowdsourcing data, we
reconstructed absolute quality scale values according to Thurstone's model. As
a result, we obtained a re-ranking of the 155 participating algorithms w.r.t.
the visual quality of the interpolated frames. This re-ranking not only shows
the necessity of visual quality assessment as another evaluation metric for
optical flow and frame interpolation benchmarks, the results also provide the
ground truth for designing novel image quality assessment (IQA) methods
dedicated to perceptual quality of interpolated images. As a first step, we
proposed such a new full-reference method, called WAE-IQA. By weighing the
local differences between an interpolated image and its ground truth WAE-IQA
performed slightly better than the currently best FR-IQA approach from the
literature.
