Jayashankar, TejasTejasJayashankarMoulin, PierrePierreMoulinTHIERRY BLUGilliam, ChrisChrisGilliam2024-03-072024-03-072019-09-01978153866249615224880https://scholars.lib.ntu.edu.tw/handle/123456789/640470High-quality video frame interpolation often necessitates accurate motion estimation, which can be obtained using modern optical flow methods. In this paper, we use the recently proposed Local All-Pass (LAP) algorithm to compute the optical flow between two consecutive frames. The resulting flow field is used to perform interpolation using cubic splines. We compare the interpolation results against a well-known optical flow estimation algorithm as well as against a recent con-volutional neural network scheme for video frame interpolation. Qualitative and quantitative results show that the LAP algorithm performs fast, high-quality video frame interpolation, and perceptually outperforms the neural network and the Lucas-Kanade method on a variety of test sequences.Convolutional neural network | Lucas-Kanade algorithm | Optical flow | Splines | Video interpolationconference paper10.1109/ICIP.2019.88034842-s2.0-85076801379https://api.elsevier.com/content/abstract/scopus_id/85076801379