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

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-b...

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Bibliographic Details
Published in:Symmetry (Basel) 2020-05, Vol.12 (5), p.840
Main Authors: Lee, Junggi, Kong, Kyeongbo, Bae, Gyujin, Song, Woo-Jin
Format: Article
Language:English
Subjects:
Algorithms
Applied research
Artificial neural networks
block matching
block-based motion
deep neural network
Estimation theory
Feature extraction
Human locomotion
Image processing
Matching
Methods
motion estimation
Motion simulation
Neural networks
representative matching
Upconversion
Video compression
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Summary: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.
ISSN:2073-8994
2073-8994
DOI:10.3390/sym12050840