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Communication Dans Un Congrès Année : 2022

Deep Video Frame Rate Up-conversion Network using Feature-based Progressive Residue Refinement a

Résumé

In this paper, we propose a deep learning-based network for video frame rate up-conversion (or video frame interpolation). The proposed optical flow-based pipeline employs deep features extracted to learn residue maps for progressively refining the synthesized intermediate frame. We also propose a procedure for finetuning the optical flow estimation module using frame interpolation datasets, which does not require ground truth optical flows. This procedure is effective to obtain interpolation task-oriented optical flows and can be applied to other methods utilizing a deep optical flow estimation module. Experimental results demonstrate that our proposed network performs favorably against state-of-the-art methods both in terms of qualitative and quantitative measures.
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Dates et versions

hal-03432380 , version 1 (17-11-2021)

Identifiants

  • HAL Id : hal-03432380 , version 1

Citer

Jinglei Shi, Xiaoran Jiang, Christine Guillemot. Deep Video Frame Rate Up-conversion Network using Feature-based Progressive Residue Refinement a. VISAPP 2022 - 17th International Conference on Computer Vision Theory and Applications, Feb 2022, online, France. pp.1-9. ⟨hal-03432380⟩
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