TY - UNPB
T1 - Technical report on visual quality assessment for frame interpolation
AU - Men, Hui
AU - Lin, Hanhe
AU - Hosu, Vlad
AU - Maurer, Daniel
AU - Bruhn, Andrés
AU - Saupe, Dietmar
N1 - Funded by the Deutsche Forschungsgemeinschaft (DFG,
German Research Foundation) – Projektnummer 251654672 –
TRR 161 (Project A05 and B04).
PY - 2019/3/26
Y1 - 2019/3/26
N2 - 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.
AB - 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.
KW - visual quality assessment
KW - optical flow
KW - frame interpolation
U2 - 10.48550/arXiv.1901.05362
DO - 10.48550/arXiv.1901.05362
M3 - Preprint
BT - Technical report on visual quality assessment for frame interpolation
PB - arXiv
CY - Cornell University
ER -