Subjective annotation for a frame interpolation benchmark using artefact amplification
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RESEARCH ARTICLE
Subjective annotation for a frame interpolation benchmark using artefact amplification Hui Men1 · Vlad Hosu1 · Hanhe Lin1 · Andrés Bruhn2 · Dietmar Saupe1 Received: 2 December 2019 © The Author(s) 2020
Abstract 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. It contains interpolated frames from 155 methods applied to each of 8 contents. For this purpose, 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 (3720 comparisons of 20 votes each) 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, which weights the local differences between an interpolated image and its ground truth. Keywords Visual quality assessment · Frame interpolation · Artefact amplification · Weighted error
Introduction Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – Project-ID 251654672 – TRR 161 (Project A05 and B04). * Hui Men hui.3.men@uni‑konstanz.de Vlad Hosu vlad.hosu@uni‑konstanz.de Hanhe Lin hanhe.lin@uni‑konstanz.de Andrés Bruhn [email protected]‑stuttgart.de Dietmar Saupe dietmar.saupe@uni‑konstanz.de 1
Department of Computer and Information Science, University of Konstanz, Konstanz, Germany
Institute for Visualization and Interactive Systems, University of Stuttgart, Stuttgart, Germany
2
As one of the basic video processing techniques, frame interpolation, namely computing interpolated in-between images in image sequences, is a necessary step in numerous applications such as temporal up-sampling for generating slow-motion videos [17], nonlinear video re-timing in special effects movie editing [24], a
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