Multimedia Tools and Applications

, Volume 59, Issue 2, pp 585–599 | Cite as

Semantic scalability using tennis videos as examples

  • Jui-Hsin Lai
  • Shao-Yi Chien


With advances in broadcasting technologies, people are now able to watch videos on devices such as televisions, computers, and mobile phones. Scalable video provides video bitstreams of different size under different transmission bandwidths. In this paper, a semantic scalability scheme with four levels is proposed, and tennis videos are used as examples in experiments to test the scheme. Rather than detecting shot categories to determine suitable scaling options for Scalable Video Coding (SVC) as in previous studies, the proposed method analyzes a video, transmits video content according to semantic priority, and reintegrates the extracted contents in the receiver. The purpose of the lower bitstream size in the proposed method is to discard video content of low semantic importance instead of decreasing the video quality to reduce the video bitstream. The experimental results show that visual quality is still maintained in our method despite reducing the bitstream size. Further, in a user study, we show that evaluators identify the visual quality as more acceptable and the video information as clearer than those of SVC. Finally, we suggest that the proposed scalability scheme in the semantic domain, which provides a new dimension for scaling videos, can be extended to various video categories.


Content adaptive Scalable video Video rendering Video analysis Scalable video coding 


  1. 1.
    Abdollahian G, Taskiran C, Pizlo Z, Delp E (2010) Camera motion based analysis of user generated video. IEEE Trans Multimedia 12(1):28–41CrossRefGoogle Scholar
  2. 2.
    Akyol E, Tekalp AM, Civanlar MR (2007) Content-aware scalability-type selection for rate adaptation of scalable video. EURASIP J. Appl Signal Process 7(1):214–225Google Scholar
  3. 3.
    Han J, Farin D, de With PHN (2008) Broadcast court-net sports video analysis using fast 3-d camera modeling. IEEE Trans Circuits Syst Video Technol 18(11):1628–1638CrossRefGoogle Scholar
  4. 4.
    Inamoto N, Saito H (2004) Free viewpoint video synthesis and presentation of sporting events for mixed reality entertainment. In: Proceedings of the 2004 ACM SIGCHI International Conference on Advances in Computer Entertainment Technology, pp 42–50Google Scholar
  5. 5.
    Lai J-H, Chien, S-Y (2008) Baseball and tennis video annotation with temporal structure decomposition. In: Proceedings of IEEE International Workshop on Multimedia Signal Processing, MMSP 2008, pp 676–679Google Scholar
  6. 6.
    Lai J-H, Chien S-Y (2008) Tennis video enrichment with content layer separation and real-time rendering in sprite plane. In: Proceedings of IEEE International Workshop on Multimedia Signal Processing, MMSP 2008, pp 672–675Google Scholar
  7. 7.
    Lai J-H, Kao C-C, Chien S-Y (2009) Super-resolution sprite with foreground removal. In: Proceedings of IEEE International Conference on Multimedia and Expo, pp 1306–1309Google Scholar
  8. 8.
    Liang D, Huang Q, Liu Y, Zhu G, Gao W (2007) Video2cartoon: generating 3d cartoon from video2cartoon: generating 3d cartoon from broadcast soccer video. IEEE Trans. Circuits Electron 53(3):1138–1146CrossRefGoogle Scholar
  9. 9.
    Liu T, Yuan Z, Sun J, Wang J, Zheng N, Tang X (2010) Learning to detect a salient object. IEEE Trans Pattern Anal Mach Intell 33(3):1–15MATHGoogle Scholar
  10. 10.
    Lai J-H, Chien S-Y (2009) Tennis video with semantic scalability. In: Proceedings of 11th IEEE International Symposium on Multimedia, pp 523–526Google Scholar
  11. 11.
    Recommendation JPEG Standard (1993) ITU-TGoogle Scholar
  12. 12.
    Recommendation H.264 (2003) Advanced video coding for generic audiovisual services. ITU-TGoogle Scholar
  13. 13.
    Tang Q, Koprinska I, Jin JS (2005) Content-adaptive transmission of reconstructed soccer goal events over low bandwidth networks. In: Proceedings of the 13th Annual ACM International Conference on Multimedia, MULTIMEDIA ’03, pp 271–274Google Scholar
  14. 14.
    Thang TC, Kim J-G, Kang JW, Yoo J-J (2009) Svc adaptation: standard tools and supporting methods. Elsevier Signal Process. Image Commun 24(3):214–228CrossRefGoogle Scholar
  15. 15.
    Wang Y, van der Schaar M, Chang S-F, Loui A (2005) Classification-based multidimensional adaptation prediction for scalable video coding using subjective quality evaluation. IEEE Trans Circuits Syst Video Technol 15(10):1270–1279CrossRefGoogle Scholar
  16. 16.
    Wikstrand G, Eriksson S (2002) Football animations for mobile phones. In: Proceedings of the Second Nordic Conference on Human-Computer Interaction NordiCHI ’02, pp 255–258Google Scholar
  17. 17.
    Yu X, Xu C, Leong HW, Tian Q, Tang Q, Wan KW (2003) Trajectory-based ball detection and tracking with applications to semantic analysis of broadcast soccer video. In: Proceedings of the Eleventh ACM International Conference on Multimedia, pp 11–20Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  1. 1.Media IC and System Lab, Graduate Institute of Electronics EngineeringNational Taiwan UniversityTaipeiTaiwan

Personalised recommendations