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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish
Title of host publication2021 IEEE 10th Global Conference on Consumer Electronics, GCCE 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages278-279
Number of pages2
ISBN (Electronic)9781665436762
DOIs
Publication statusPublished - 2021
Event10th IEEE Global Conference on Consumer Electronics, GCCE 2021 - Kyoto, Japan
Duration: 2021 Oct 122021 Oct 15

Publication series

Name2021 IEEE 10th Global Conference on Consumer Electronics, GCCE 2021

Conference

Conference10th IEEE Global Conference on Consumer Electronics, GCCE 2021
Country/TerritoryJapan
CityKyoto
Period21/10/1221/10/15

Keywords

  • Deformable Convolution
  • Versatile Video Coding (VVC)
  • video super resolution

ASJC Scopus subject areas

  • Computer Science Applications
  • Signal Processing
  • Biomedical Engineering
  • Electrical and Electronic Engineering
  • Media Technology
  • Instrumentation

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