콘텐츠 본문
논문 해외 국제일반학술지 A high-quality frame rate up-conversion technique for Super SloMo
- 학술지 구분 국제일반학술지
- 게재년월 2021-08
- 저자명 Minseop Kim and Haechul Choi
- 학술지명 IJCVR
- 발행처명 IJCVR
- 발행국가 해외
- 논문언어 외국어
- 전체저자수 2
논문 초록 (Abstract)
In this paper, we propose several methods to improve Super SloMo, a deep learning-based frame rate up-conversion technique for the temporal quality improvement of video. In the proposed methods, the training dataset and hyper-parameter are changed and trained to obtain optimal results while maintaining the existing network structure of Super SloMo. The first method improves the cognition of images when trained with the validation set of characteristics similar to the training set. The second method reduces video loss in all validation sets when trained by adjusting the hyper-parameters of the error function value. The experimental results show that the two proposed methods improved the peak signal-to-noise ratio and the mean of the structural similarity index by 0.11 dB and 0.033% with the specialised training set and by 0.37 dB and 0.077% via adjusting the reconstruction and warping loss parameters, respectively.
