Abstract
Recent video compression algorithms such as the members of the MPEG or H.26x family use image transformations to store individual frames, and motion compensation between these frames. In contrast, the video codec presented here is a model-based approach that encodes fore- and background independently. It is well-suited for applications with static backgrounds, i.e. for applications such as traffic or security surveillance, or video conferencing. Our video compression algorithm tracks moving foreground objects and stores the obtained poses. Furthermore, a compressed version of the background image and some other information such as 3-D object models are encoded. In a second step, recent halftoning and PDE-based image compression algorithms are employed to compress the encoding error. Experiments show that the stored videos can have a significantly better quality than state-of-the-art algorithms such as MPEG-4.
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References
Abomhara, M., Khalifa, O.O., Zakaria, O., Zaidan, A., Zaidan, B., Rame, A.: Video compression techniques: An overview. Journal of Applied Sciences 10(16), 1834–1840 (2010)
Artigas, X., Torres, L.: A model-based enhanced approach to distributed video coding. In: Workshop on Image Analysis for Multimedia Interactive Services (WIAMIS 2005), Article No. 1127 (2005)
Belhachmi, Z., Bucur, D., Burgeth, B., Weickert, J.: How to choose interpolation data in images. SIAM Journal on Applied Mathematics 70(1), 333–352 (2009)
Eisert, P., Girod, B.: Facial expression analysis for model-based coding of video sequences. In: Proc. Picture Coding Symposium, Berlin, pp. 33–38 (1997)
Eisert, P., Girod, B.: Model-based coding of facial image sequences at varying illumination conditions. In: Proc. 10th Image and Multidimensional Digital Signal Processing Workshop, Alpbach, pp. 119–122 (1998)
Forchheimer, R., Fahlander, O.: Low bit-rate coding through animation. In: Proceedings of Picture Coding Symposium, pp. 113–114 (March 1983)
Granai, L., Vlachos, T., Hamouz, M., Tena, J.R., Davies, T.: Model-based coding of 3D head sequences. In: Proc. 3DTV Conference. IEEE Computer Society Press (2007)
Iijima, T.: Basic theory on normalization of pattern (in case of typical one-dimensional pattern). Bulletin of the Electrotechnical Laboratory 26, 368–388 (1962) (in Japanese)
ISO/IEC: Information technology – lossy/lossless coding of bi-level images (2001), ISO/IEC 14492. Latest corrections in 2004 (2004)
Javůrek, R.: Model based facial video sequences coding. In: Radioelektronika 2003 – Conference Proceedings, pp. 115–118 (2003)
Mahoney, M.: Data compression programs (2009), http://mattmahoney.net/dc/ (last visited November 30, 2009)
Pandzic, I.S., Forchheimer, R. (eds.): MPEG-4 Facial Animation: The Standard, Implementation and Applications. Wiley, New York (2003)
Pardàs, M., Bonafonte, A.: Facial animation parameters extraction and expression detection using hidden markov models. In: Signal Processing: Image Communication, vol. 17, pp. 675–688 (2002)
Pearson, D.E.: Developments in model-based video coding. Proceedings of the IEEE 83(6), 892–906 (1995)
Schmaltz, C., Gwosdek, P., Bruhn, A., Weickert, J.: Electrostatic halftoning. Computer Graphics Forum 29(8), 2313–2327 (2010)
Schmaltz, C., Rosenhahn, B., Brox, T., Weickert, J.: Localised Mixture Models in Region-Based Tracking. In: Denzler, J., Notni, G., Süße, H. (eds.) DAGM 2009. LNCS, vol. 5748, pp. 21–30. Springer, Heidelberg (2009)
Schmaltz, C., Weickert, J., Bruhn, A.: Beating the Quality of JPEG 2000 with Anisotropic Diffusion. In: Denzler, J., Notni, G., Süße, H. (eds.) DAGM 2009. LNCS, vol. 5748, pp. 452–461. Springer, Heidelberg (2009)
Sigal, L., Balan, A.O., Black, M.J.: HUMANEVA: Synchronized video and motion capture dataset and baseline algorithm for evaluation of articulated human motion. International Journal of Computer Vision 87(1/2), 4–27 (2010)
Sullivan, G.J., Wiegand, T.: Video compression – from concepts to the H.264/AVC standard. Proceedings of the IEEE 93(1), 18–31 (2005)
Toelg, S., Poggio, T.: Towards an example-based image compression architecture for video-conferencing. Tech. Rep. AIM-1494, Massachusetts Institute of Technology, Cambridge, MA, USA (1994)
Vieux, W.E., Schwerdt, K., Crowley, J.L.: Face-Tracking and Coding for Video Compression. In: Christensen, H.I. (ed.) ICVS 1999. LNCS, vol. 1542, pp. 151–161. Springer, Heidelberg (1998)
Weickert, J.: Theoretical foundations of anisotropic diffusion in image processing. Computing Supplement 11, 221–236 (1996)
Yao, Z.: Model-based Coding – Initialization, Parameters Extraction and Evaluation. Ph.D. thesis, Department of Applied Physics and Electronics, Umeå University, Sweden (January 2005)
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Schmaltz, C., Weickert, J. (2012). Video Compression with 3-D Pose Tracking, PDE-Based Image Coding, and Electrostatic Halftoning. In: Pinz, A., Pock, T., Bischof, H., Leberl, F. (eds) Pattern Recognition. DAGM/OAGM 2012. Lecture Notes in Computer Science, vol 7476. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32717-9_44
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