ETRI Journal

  • 제44권1호
  • /
  • Pages.73-84
  • /
  • 2022
  • /
  • 1225-6463(pISSN)
  • /
  • 2233-7326(eISSN)
한국전자통신연구원 (Electronics and Telecommunications Research Institute)
권호 표지
DOI QR코드

DOI QR Code

Enhanced pruning algorithm for improving visual quality in MPEG immersive video

  • Shin, Hong-Chang (Realistic-Media Research Section, Electronics and Telecommunications Research Institute) ;
  • Jeong, Jun-Young (Realistic-Media Research Section, Electronics and Telecommunications Research Institute) ;
  • Lee, Gwangsoon (Realistic-Media Research Section, Electronics and Telecommunications Research Institute) ;
  • Kakli, Muhammad Umer (Realistic-Media Research Section, Electronics and Telecommunications Research Institute) ;
  • Yun, Junyoung (Department of Computer Science, College of Engineering, Hanyang University) ;
  • Seo, Jeongil (Realistic-Media Research Section, Electronics and Telecommunications Research Institute)
  • 투고 : 2021.06.22
  • 심사 : 2021.09.27
  • 발행 : 2022.02.01
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

The moving picture experts group (MPEG) immersive video (MIV) technology has been actively developed and standardized to efficiently deliver immersive video to viewers in order for them to experience immersion and realism in various realistic and virtual environments. Such services are provided by MIV technology, which uses multiview videos as input. The pruning process, which is an important component of MIV technology, reduces interview redundancy in multiviews videos. The primary aim of the pruning process is to reduce the amount of data that available video codec must handle. In this study, two approaches are presented to improve the existing pruning algorithm. The first method determines the order in which images are pruned. The amount of overlapping region between the source views is then used to determine the pruning order. The second method considers global region-wise color similarity to minimize matching ambiguity when determining the pruning area. The proposed methods are evaluated under common test condition of MIV, and the results show that incorporating the proposed methods can improve both objective and subjective quality.

키워드

과제정보

This work was supported by Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No. 2018-0-00207, Immersive Media Research Laboratory).

참고문헌

  1. R. Koenen and M. L. Champel, Requirements MPEG-I Phase 1b l, Standard ISO/IEC JTC1/SC29/WG11 MPEG/N7331, Gwangju, South Korea, Jan. 2018.
  2. K. Muller, P. Merkle, and T. Wiegand, 3-D video representation using depth maps, Proc. IEEE 99 (2011), no. 4, 643-656. https://doi.org/10.1109/JPROC.2010.2091090
  3. O. Stankiewicz, G. Lafruit, and M. Domanski, Multiview video: Acquisition, processing, compression and virtual view rendering, in Academic Press Library in Signal Processing: Image and Video Processing and Analysis and Computer Vision, vol. 6, Academic, New York, NY, USA, 2018, pp. 3-74.
  4. B. Salahieh et al., Test model 6 for immersive video, standard ISO/IEC JTC1/SC29/WG11MPEG/N19483, July 2020.
  5. J. Jung and B. Kroon, Common test conditions for MPEG immersive video, ISO/IEC JTC 1/SC 29/WG 11 N19484, 2020.
  6. J. Shade et al., Layered depth images, in Proc. Annu. Conf. Comput. Graph. Interact. Techn. (New York, NY, USA), July 1998, 231-242.
  7. K. Muller et al., Reliability-based generation and view synthesis in layered depth video, in Proc. IEEE Workshop Multimed. Signal Process. (Cairns, Australia), Oct. (2008), 34-39.
  8. C. Zhu et al., 3D-TV System With Depth-Image-Based Rendering, Springer, New York, NY, USA, 2012.
  9. Y. Ye, E. Alshina, and J. Boyce, Algorithm descriptions of projection format conversion and video quality metrics in 360Lib, Document ITU-T SG16 WP3 ISO/IEC JTC1/SC29/W 11, JVET F-1003, Hobart, Australia, Apr. 2017.
  10. Y. Sun, A. Lu, and L. Yu, Weighted-to-spherically-uniform quality evaluation for omnidirectional video, IEEE Signal Process. Lett. 24 (2017), no. 9, 1408-1412. https://doi.org/10.1109/LSP.2017.2720693
  11. Z. Li et al., Toward a practical perceptual video quality metric, Netflix Technology Blog, June 6, 2016.
  12. A. Dziembowski, Software Manual of IV-PSNR for Immersive Video, Standard ISO/IEC JTC1/SC29/WG11 MPEG/N18709, Goteborg, Sweden, July 2019.