High-quality video frame interpolation often necessitates accurate motion estimates between consecutive frames. Standard video encoding schemes often
estimate the motion between frames using variants of block matching algorithms. For the sole purposes of video frame interpolation, more accurate
estimates can be obtained using modern optical
flow methods.
In this thesis, 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 convolutional 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. We also perform a case study to compare
LAP interpolated frames against those obtained using two leading methods
on the Middlebury optical flow
benchmark. Finally, we perform a user study
to gauge the correlation between the quantitative and qualitative results.Ope
