GOP Structure Adaptable to the Location of Shot Cuts

• Autorzy: Krulikovská L. , Polec J.
• Języki publikacji: EN

Abstrakty

EN

In this paper we present a novel two stage algorithm for improving video coding efficiency. The proposed method combines video cut detection and adaptive GOP structure. At first, we have proposed a new technique of frames' comparison for the shot cut detection. The majority of existing methods compare pairs of successive frames. We compare actual frame with its motion estimated prediction. We also present adaptive threshold. The efficiency of novel technique for video cut detection was confirmed through experiment and compared to the commonly used ones in the terms of recall and precision. The next step is to situate I frames to the positions of detected cuts during the process of video encoding. Finally the proposed method is verified by simulations and the obtained results are compared with fixed GOP structures of sizes 4, 8, 12, 16, 32, 64, 128 and GOP structure with length of entire video. Proposed method achieved the gain in bit rate from 15,33% to 50,59%, while not degrading PSNR in comparison to simulated fixed GOP structures.

Słowa kluczowe

EN

shot cut detection; Pearson correlation coefficient; motion estimation; adaptive threshold; video encoding; adaptive GOP structure

• Wydawca: Polish Academy of Sciences, Committee of Electronics and Telecommunication
• Czasopismo: International Journal of Electronics and Telecommunications
• Rocznik: 2012
• Tom: Vol. 58, No. 2
• Strony: 129--134
• Opis fizyczny: Bibliogr. 17 poz., il., wykr.

Twórcy

autor

Krulikovská L.

autor

Polec J.

• Institute of Telecommunications, Faculty of Electrical Engineering and Information Technology, Slovak University of Technology, Ilkovicova 3, Bratislava, SK-812 19, Slovak Republic,

Bibliografia

• [1] Z. Cernekova, “Temporal video segmentation and video summarization,” Ph. D. dissertation, Comenius Univ., Bratislava, 2009.
• [2] A. Amiri and M. Fathy, “Video shot boundary detection using qr decomposition and gaussian transition detection,” EURASIP Journal on Advances in Signal Processing, 2009.
• [3] A. Hanjalic, “Shot-boundary detection: unraveled and resolved?” IEEE Transactions on Circuits and Systems for Video Technology, vol. 12, no. 2, pp. 90-105, 2002.
• [4] J. S. Boreczky and L. A. Rowe, “Comparison of video shot boundary detection techniques,” in Storage and Retrieval for Still Image and Video Databases IV, 1996, pp. 170-179.
• [5] R. Lienhart, “Comparison of automatic shot boundary detection algorithms,” in Storage and Retrieval for Still Image and Video Databases VII, 1999, pp. 290-301.
• [6] R. M. M. R. Naphade, “A high-performance shot boundary detection algorithm using multiple cues,” in Proc. IEEE Int. Conf. on Image Proc., vol. 2, 1998, pp. 884-887.
• [7] C. Taskiran and E. J. Delp, “Video scene change detection using the generalized sequence trace,” in Proc. IEEE Int. Conf. on Image Proc., 1998, pp. 2961-2964.
• [8] J. K. Y. Yusoff and W. Christmas, “Combining multiple experts for classifying shot changes in video sequences,” in Proc. 6th Int. Conf. On Multimedia Comp. and Systems (ICMCS), 1999, pp. 700-704.
• [9] K. R. R. Dugad and N. Ahuja, “Robust video shot change detection,” in IEEE Workshop on Multimedia Signal Processing, 1998.
• [10] B. L. Yeo and B. Liu, “Rapid scene analysis on compressed video,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 5, no. 6, pp. 533-544, 1995.
• [11] J. M. R. Zabih and K. Mai, “A feature-based algorithm for detecting and classifying production effects,” ACM Multimedia Systems, vol. 7, no. 2, pp. 119-128, 1999.
• [12] ITU-T, “Draft itut recommendation and final draft international standard of joint video specification (itu-t rec.h.264 - iso/iec 14496 - 10 avc),” International Telecommunication Union, Tech. Rep. ITU-T Rec.H.264 - ISO/IEC 14496 - 10 AVC, 2003.
• [13] G. B. T. Wiegand, G. J. Sullivan and A. Luthra, “Overview of the h.264/avc video coding standard,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 13, no. 7, pp. 560-576, 2003.
• [14] D. Zhang and G. Lu, “Review of shape representation and description techniques,” Pattern Recognition, vol. 37, no. 1, pp. 1-19, 2004.
• [15] S. C. C. S. C. Hsia and C. L. Chen, “A real-time chip implementation for adaptive video coding control,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 14, no. 8, pp. 1098-1104, 2004.
• [16] Y. K. Eugene and R. G. Johnston, “The ineffectiveness of the correlation coefficient for image comparisons,” Los Alamos, Tech. Rep. LA-UR-96-2474, 1996.
• [17] J. S. B. Zatt, M. Porto and S. Bampi, “Gop structure adaptive to the video content for efficient h.264/avc encoding,” in Proceedings of 2010 IEEE 17th International Conference on Image Processing, SEPTEMBER 2010, pp. 3053-3056.