Bi‐directional deformable block‐based motion estimation for frame rate‐up conversion of 360‐degree videos

2017 ◽  
Vol 53 (17) ◽  
pp. 1192-1194 ◽  
Author(s):  
Nayoung Kim ◽  
Je‐Won Kang
Keyword(s):  
Motion Estimation ◽  
Frame Rate ◽  
Block Based ◽  
Frame Rate Up Conversion

scholarly journals BlockNet: A Deep Neural Network for Block-Based Motion Estimation Using Representative Matching

Symmetry ◽  
2020 ◽  
Vol 12 (5) ◽  
pp. 840
Author(s):  
Junggi Lee ◽  
Kyeongbo Kong ◽  
Gyujin Bae ◽  
Woo-Jin Song
Keyword(s):  
Motion Estimation ◽  
Computational Cost ◽  
Frame Rate ◽  
Global Motion ◽  
Neural Architecture ◽  
Block Motion ◽  
Block Based ◽  
Frame Rate Up Conversion ◽  
Coarse To Fine ◽  
Simple Average

Owing to the limitations of practical realizations, block-based motion is widely used as an alternative for pixel-based motion in video applications such as global motion estimation and frame rate up-conversion. We hereby present BlockNet, a compact but effective deep neural architecture for block-based motion estimation. First, BlockNet extracts rich features for a pair of input images. Then, it estimates coarse-to-fine block motion using a pyramidal structure. In each level, block-based motion is estimated using the proposed representative matching with a simple average operator. The experimental results show that BlockNet achieved a similar average end-point error with and without representative matching, whereas the proposed matching incurred 18% lower computational cost than full matching.

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