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A Study on Fingerprinting Robustness Indicators for Immersive 360-degree Video

실감형 360도 영상 특징점 기술 강인성 지표에 관한 연구

  • Kim, Youngmo (Dept. of Computer Science and Engineering, Soongsil University) ;
  • Park, Byeongchan (Dept. of Computer Science and Engineering, Soongsil University) ;
  • Jang, Seyoung (Dept. of Computer Science and Engineering, Soongsil University) ;
  • Yoo, Injae (Research Institute, Beyondtech Inc.) ;
  • Lee, Jaechung (Research Institute, Beyondtech Inc.) ;
  • Kim, Seok-Yoon (Dept. of Computer Science and Engineering, Soongsil University)
  • Received : 2020.08.31
  • Accepted : 2020.09.11
  • Published : 2020.09.30

Abstract

In this paper, we propose a set of robustness indicators for immersive 360-degree video. With the full-fledged service of mobile carriers' 5G networks, it is possible to use large-capacity, immersive 360-degree videos at high speed anytime, anywhere. Since it can be illegally distributed in web-hard and torrents through DRM dismantling and various video modifications, however, evaluation indicators that can objectively evaluate the filtering performance for copyright protection are required. In this paper, a robustness indicators is proposed that applies the existing 2D Video robustness indicators and considers the projection method and reproduction method, which are the characteristics of Immersive 360-degree Video. The performance evaluation experiment has been carried out for a sample filtering system and it is verified that an excellent recognition rate of 95% or more has been achieved in about 3 second execution time.

본 논문에서는 실감형 360도 영상의 특징점 기술의 강인성 지표를 제안한다. 이동통신사의 5G 서비스가 본격적으로 서비스됨에 따라 언제 어디서든지 대용량의 실감형 360도 영상저작물을 빠른 속도로 이용할 수 있게 되었다. 그러나 DRM 해체 및 각종 영상 변형을 통해 웹하드, 토렌트 등에서 불법 유통되고 있어 저작권 보호를 위한 강인성이 지원되는 필터링 기술이 요구되고 있다. 본 논문에서는 기존 2D 영상의 강인성 지표를 응용하고 실감형 360도 영상저작물의 특징인 투영방법 및 재생 방법을 고려한 강인성 지표를 제안하였다. 본 제안을 샘플 필터링 시스템에 대하여 적용하여 성능평가를 실시하였고 약 3초대의 실행시간에서 95% 이상의 우수한 인식률을 확인하였다.

Keywords

References

  1. J. S. Lee, "Changes in realistic media content distribution environment and production technology in the 5G era," National IT Industry Promotion Agency Issue Report, No.22, 2019.
  2. J. H. Park, "5G Era, Content Industry Changes and Implications," KIET Industrial Economy, 2019.
  3. K. Y. Choi "Strategies for Activating Realistic Content Industry for Leading the 5G Era('19-'23)," Korea VR.AR Industry Association, 2019.
  4. VideoPlus "5G Era, Single Media Copyright Issues and Trends," VidoePlus, 2019.11.06.
  5. M. G. Kim, "韓5G 기반 VR.AR 육성 급한데...'법.정책' 없어 '막막'," EYENEWS 24, 2019.
  6. Y. M. Kim, W. G. Kim, J. J. Lee, S. H. Jho and D. M. Shin, "Performance Evaluation of Video Contents Filtering," Telecommunication Technology Association Standard, 2013. TTAK. KO-12.0161/R1.
  7. Korea Copyright Commission, Performance Evaluation of Feature-based Filtering, https://www.copyright.or.kr/kcc/tmis/performance/filtering/init.do
  8. S. J. Oh, "MPEG Omnidirectional Media Format (OMAF) for 360 Media," Journal of Broadcast Engineering, Vol.22, No.5, pp.600-607, 2017. DOI: 10.5909/JBE.2017.22.5.600
  9. J. W. Lee, "Immersive Media Format Standardization Trend," Broadcast and Media Magazine, Vol.24, No.4, pp.343-352. 2017. DOI: 10.22648/ETRI.2019.J.340614
  10. G. S. Lee, J. Y. Jeong, H. C. Shin and J. I. Seo, "Standardization Trend of 3DoF+ Video for Immersive Media," Electronics and Telecommunications Trends, Vol.34, No.6, pp.156-163, 2019. DOI: 10.22648/ETRI.2019.J.340614
  11. ISO/IEC 23090-2:2019, "Information technology-Coded representation of immersive media-Part 2: Omnidirectional media format," MPEG
  12. B. C. Park, S. Y. Jang, I. J. Yoo, J. C. Lee, S. Y. Kim and Y. M. Kim, "A Feature Point Extraction and Identification Technique for Immersive Contents Using Deep Learning," j.inst.Korean.electr. electron.eng, Vol.24, No.2, pp.529-535, 2020. DOI: 10.7471/ikeee.2020.24.2.529
  13. B. C. Park, S. Y. Jang, I. J. Yoo, J. C. Lee, S. Y. Kim and Y. M. Kim, "A Feature Point Recognition Ratio Improvement Method for Immersive Contents Using Deep Learning," j.inst. Korean.electr.electron.eng, Vol.24, No.2, pp.419-425, 2020. DOI: 10.7471/ikeee.2020.24.2.419

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