A Study of Video Super-Resolution Method Using Video Coded Data as Training Data

Remina Yano, Yun Liu, Hiroshi Watanabe, Takuya Suzuki, Takeshi Chujoh, Tomohiro Ikai

研究成果: Conference contribution

抄録

This paper presents a study of video super resolution method by applying Deformable Convolution network to coded video. It is reported the effectiveness of using coded video as training data, and numerical and visual results of coded fine-tuned model. From those results, it is discussed the relationship between about characteristic of training data and especially in video's framerate. There it is shown that video sequence which has similar framerate with training data can perform higher PSNR, since low framerate means the motion between frames is large and Deformable Convolution can learn that large motion.

本文言語English
ホスト出版物のタイトル2021 IEEE 10th Global Conference on Consumer Electronics, GCCE 2021
出版社Institute of Electrical and Electronics Engineers Inc.
ページ278-279
ページ数2
ISBN(電子版)9781665436762
DOI
出版ステータスPublished - 2021
イベント10th IEEE Global Conference on Consumer Electronics, GCCE 2021 - Kyoto, Japan
継続期間: 2021 10月 122021 10月 15

出版物シリーズ

名前2021 IEEE 10th Global Conference on Consumer Electronics, GCCE 2021

Conference

Conference10th IEEE Global Conference on Consumer Electronics, GCCE 2021
国/地域Japan
CityKyoto
Period21/10/1221/10/15

ASJC Scopus subject areas

  • コンピュータ サイエンスの応用
  • 信号処理
  • 生体医工学
  • 電子工学および電気工学
  • メディア記述
  • 器械工学

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