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Contextual cropping and scaling of TV productions

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Abstract

In this paper, an application is presented which automatically adapts SDTV (Standard Definition Television) sports productions to smaller displays through intelligent cropping and scaling. It crops regions of interest of sports productions based on a smart combination of production metadata and systematic video analysis methods. This approach allows a context-based composition of cropped images. It provides a differentiation between the original SD version of the production and the processed one adapted to the requirements for mobile TV. The system has been comprehensively evaluated by comparing the outcome of the proposed method with manually and statically cropped versions, as well as with non-cropped versions. Envisaged is the integration of the tool in post-production and live workflows.

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References

  1. Arthur C (2007) Television is a turnoff for mobile users. The Guardian. http://www.guardian.co.uk/technology/2007/aug/02/guardianweeklytechnologysection.mobilephones. Accessed 26 June 2010

  2. Bouguet JY (1999) Pyramidal implementation of the Lucas Kanade feature tracker description of the algorithm. Intel Corporation Microprocessor Research Labs

  3. Chen L-Q, Xie X, Fan X, Ma W-Y, Zhang H-J, Zhou H-Q (2003) A visual attention model for adapting images on small displays. ACM Multimed Syst J 9(4):353–364

    Article  Google Scholar 

  4. Cheng W-H, Chu W-T, Wu J-L (2005) A visual attention based region-of-interest determination framework for video sequences. IEICE Trans Inf Syst E-88D(7):1578–1586

    Article  Google Scholar 

  5. Dearden A, Demiris Y, Grau O (2006) Tracking football player movement from a single moving camera using particle filters. Proceedings of the 3rd European Conference on Visual Media Production (CVMP), London. pp 29–37

  6. Deigmoeller J, Just N, Itagaki T, Stoll G (2010) An approach to intelligently crop and scale video for broadcast applications. Proceedings of the 2010 ACM Symposium on Applied Computing

  7. Deselaers T, Dreuw P, Ney H (2008) Pan, zoom, scan—time-coherent, trained automatic video cropping. IEEE Conference on Computer Vision and Pattern Recognition, Anchorage

  8. BMF Documentation (2007) BMF—Broadcast Metadata exchange Format. Institut fuer Rundfunktechnik, Version 01.00.00, Munich

  9. European Telecommunications Standards Institute (2006) Specification for the use of Video and Audio Coding in DVB services delivered directly over IP protocols. European Telecommunications Standards Institute

  10. Forsyth DA, Ponce J (2003) Computer vision—a modern approach. Prentice Hall, New Jersey

    Google Scholar 

  11. Hartley R, Zisserman A (2003) Multiple view geometry in computer vision, Second Edition. Cambridge University Press

  12. Hou X (2009) Spectral Residual, http://www.its.caltech.edu/~xhou/. Accessed 26 June 2010

  13. Hou X, Zhang L (2007) Saliency detection: a spectral residual approach. Conference on Computer Vision and Pattern Recognition, Minneapolis

    Google Scholar 

  14. International Telecommunication Union (2007) Methodology for the subjective assessment of video quality. Recommendation ITU-R BT.1788

  15. Itti L, Koch C, Niebur E (1998) A model of saliency-based visual attention for rapid scene analysis. IEEE Trans Pattern Anal Mach Intell 20:1254–1259

    Article  Google Scholar 

  16. Knee M, Piroddi R (2008) Aspect processing: the shape of things to come. International Broadcast Conference 2008, Amsterdam

    Google Scholar 

  17. Le Meur O, Le Callet P, Barba D (2007) Predicting visual fixations on video based on low-level visual features. Vis Res 47:2483–2498

    Article  Google Scholar 

  18. Lourakis M (2009). homest: A C/C++ Library for Robust, Non-linear Homography Estimation, http://www.ics.forth.gr/~lourakis/homest/. Accessed 26 June 2010

  19. Lucas BD, Kanade T (1981). An iterative image registration technique with an application to stereo vision. Proceedings of Imaging understanding workshop, pp 121–130

  20. Lum WY, Lau FCM (2003) User-centric content negotiation for effective adaptation service in mobile computing. IEEE Trans Software Eng 29(12):1100–1111

    Article  Google Scholar 

  21. Mason S (2006) Mobile TV—results from the DVB-H trial in Oxford. EBU Technical Review. http://www.ebu.ch/en/technical/trev/trev_306-mason.pdf. Accessed 26 June 2010

  22. Mohan R, Smith JR, Li C-S (1999) Adapting multimedia internet content for universal access. IEEE Trans Multimedia 1(1):104–114

    Article  Google Scholar 

  23. OpenCV library Documentation (2009) http://opencv.willowgarage.com/wiki/. Accessed 26 June 2010

  24. Ruderman DL (1994) The statistics of natural images. Comput Neural Syst 5:517–548

    Article  MATH  Google Scholar 

  25. Sachs L, Reynarowych Z (1984) Applied statistics: a handbook of techniques. Springer Verlag, New York

    MATH  Google Scholar 

  26. Treisman A (1986) Features and objects in visual processing. Sci Am 255:106–115

    Article  Google Scholar 

  27. Walther DB (2010). Saliency toolbox. http://www.saliencytoolbox.net/index.html. Accessed 26 June 2010

  28. Zaller J (2007) Snell & Wilcox’s Helios. http://broadcastengineering.com/RF/broadcasting_snell_wilcoxs_helios/index.html. Accessed 26 June 2010

  29. Zhang Z, Deriche R, Faugeras O, Luong QT (1995) A robust technique for matching two uncalibrated images through the recovery of the unknown epipolar geometry. Artif Intell 78:87–119

    Article  Google Scholar 

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Authors and Affiliations

  1. Brunel University, Uxbridge, UK

    Joerg Deigmoeller & Takebumi Itagaki

  2. Institut fuer Rundfunktechnik, Munich, Germany

    Norbert Just

  3. Munich, Germany

    Gerhard Stoll

Authors
  1. Joerg Deigmoeller
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  2. Takebumi Itagaki
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  3. Norbert Just
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  4. Gerhard Stoll
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Corresponding author

Correspondence to Joerg Deigmoeller.

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Deigmoeller, J., Itagaki, T., Just, N. et al. Contextual cropping and scaling of TV productions. Multimed Tools Appl 61, 623–644 (2012). https://doi.org/10.1007/s11042-011-0804-3

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  • DOI: https://doi.org/10.1007/s11042-011-0804-3

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