Proposition of Rubustness Indicators for Immersive Content Filtering
DOI:
https://doi.org/10.13052/jwe1540-9589.2247Keywords:
Multimedia computing, Image Processing, image recognition, image resolution, image samplingAbstract
With the full-fledged service of mobile carrier 5G networks, it is possible to use large-capacity, immersive content at high speed anytime, anywhere. It can be illegally distributed in web-hard and torrents through DRM dismantling and various transformation attacks; however, evaluation indicators that can objectively evaluate the filtering performance for copyright protection are required. Since applying existing 2D filtering techniques to immersive content directly is not possible, in this paper we propose a set of robustness indicators for immersive content. The proposed indicators modify and enlarge the existing 2D video robustness indicators to consider the projection and reproduction method, which are the characteristics of immersive content. A 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 is achieved in about 3 s of execution time.
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References
J. 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.
V. Ziegler, T. Wild, M. Uusitalo, H. Flinck, V. Räisänen, K. Hätönen, ‘Stratification of 5G evolution and Beyond 5G’, In 2019 IEEE 2nd 5G World Forum (5GWF), pp. 329–334, Sep., 2019.
J. H. Park, ‘5G Era, Content Industry Changes and Implications’, KIET Industrial Economy, 2019.
PwC, ‘Seeing is believing’, PwC, 2019.
VideoPlus “5G Era, Single Media Copyright Issues and Trends,” VidoePlus, 2019.
J. Park, J. Kim, J. Seo, S. Kim, J. Lee, ‘DNN-Based Forensic Watermark Tracking System for Realistic Content Copyright Protection’, Electronics, Vol. 12, No. 3, 553, Jan., 2023.
Y. Kim, W. Kim, J. Lee, S. Jho and D. Shin, ‘Performance Evaluation of Video Contents Filtering’, Telecommunication Technology Association Standard, 2013, TTAK. KO-12.0161/R1.
Korea Copyright Commission, Performance Evaluation of Feature-based Filtering https://www.copyright.or.kr/kcc/tmis/performance/filtering/init.do.
S. Oh, ‘MPEG Omnidirectional Media Format(OMAF) for 360 Media’, Journal of Broadcast Engineering, Vol. 22, No. 5, pp. 600–607, 2017.
ISO/IEC 23090-2, ‘Information Technology-Coded Representation of immersive media – Part 2: Ommidrectional Media Format’, ISO/IEC 23090-2:2019(E).
J. Lee, ‘Immersive Media Format Standardization Trend’, Broadcast and Media Magazine, Vol. 24, No. 4, pp. 343–352. 2017.
G. Lee, J. Jeong, H. Shin, J. Seo, ‘Standardization Trend of 3DoF+ Video for Immersive Media’, Electronics and Telecommunications Trends, Vol. 34, No. 6, pp. 156–163, 2019.
B. Park, S. Jang, I. Yoo, J. Lee, S. Kim, Y. 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.
H. Lee, O. Choi, ‘An Efficient Parameter Update Method of 360-degree VR Image Model’, International Journal of Engineering Business Management, 2019.
J. Jia, C. Tang, ‘Image Stitching using Structure Deformation’, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 30, No. 4, pp. 617–631, 2008.
D. Barath, J. Matas, ‘Graph-cut RANSAC’, Proc. of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, USA, pp. 6733–6741, 2018.
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.
