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

Symmetry

Woo-Jin Song¹  Junggi Lee¹  Kyeongbo Kong¹  Gyujin Bae² 

[1] Department of Electrical Engineering, Pohang University of Science and Technology, San 31, Hyoja-dong, Nam-gu, Pohang, Gyungbuk 37673, Korea;LG Display Co., Ltd., E2 Block LG Science Park, 30, Magokjungang 10-ro, Gangseo-gu, Seoul 07796, Korea;

关键词: motion estimation;    block-based motion;    block matching;    representative matching;    deep neural network;   

DOI  :  10.3390/sym12050840

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown