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Multiview Video Compression

Multiview Video Compression. Student: Chia-Yang Tsai Advisor: Prof. Hsueh-Ming Hang Institute of Electronics, NCTU. Outline. Introduction Coding methods Performance Conclusions. References.

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Multiview Video Compression

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  1. Multiview Video Compression Student: Chia-Yang TsaiAdvisor: Prof. Hsueh-Ming HangInstitute of Electronics, NCTU

  2. Outline • Introduction • Coding methods • Performance • Conclusions

  3. References • A. Smolic, P. Kauff, “Interactive 3-D video representation and coding technologies”, Proceedings of the IEEE, vol. 93, no. 1,pp 98-110, Jan. 2005. • ISO/IEC JTC1/SC29/WG11, “Submissions received in CfP on Multiview Video Coding”, MPEG Document M12969, Bangkok, Thailand, January 2006. • ISO/IEC JTC1/SC29/WG11, “Multiview video compression using V frames”, MPEG Document M12828, Bangkok, Thailand, January 2006.

  4. Introduction

  5. Multivew= Multiple Viewpoints • Applications of multiview • 3D video • Free viewpoints selection

  6. Multivew= Multiple Viewpoints • Reasons for multiview compression • PC is powerful enough • Increasing network bandwidth • Future 3D video

  7. MPEG Standarization • Call for proposal (N7567, Oct. 2005) • Proposal competition (M12969, Jan. 2006) • NTT and Nagoya University • Thomson and University of Southern California • KDDI Corp. • ETRI and Sejong University (=M12871) • MERL (=M12828) • KBS and Yonsei University (=M12874) • Fraunhofer-HHI (=M12945) • Technical University of Berlin

  8. Coding MethodsDisparity compensated view prediction (DCVP)View synthesis prediction (VSP)Hierarchical B frames

  9. 1 3 5 7 2 4 6 Multiview Frame Structure time view ….. . . .

  10. Block diagram • Predictions based on H.264/AVC JM95

  11. Block diagram • Decoder

  12. DCVP • DCVP= Disparity Compensated View Prediction • Problems • High spatial correlations between different viewpoints • Solution • Prediction between viewpoints

  13. B B DCVP • DCVP= Disparity Compensated View Prediction I B B P B B I B B P B B I I B B P B B I B B P B B I I P B B P B B I B B P B B I ….. I B B P B B I B B P B B I I B B P B B I B B P B B I

  14. VSP • VSP= View Synthesis Prediction • Problems • Different viewpoints have different depth • Rotation, translation speed • Solution • Synthesis virtual images before real prediction

  15. time view View Synthesis Via View Warping View Synthesis Via View Interpolation VSP R: Rotation matrix D: Depth information T: Translation matrix A: Intrinsic matrix C C’

  16. How to get depths? • From camera record • From well-known computer vision algorithms • Block-based depth search where || I [c,t,x,y] –I [c’,t,x’,y’] || denotes the average error between the block at (x,y) in camera c at time t

  17. How to get depths? • Depths map: • Left: computer vision algorithm • Right: block based depth search • Compression result: • Depth information: 5-10% total bitrates • Left and right have equal performance

  18. Prediction structure

  19. Hierarchical B pictures • Hierarchical B pictures • Fully compatible to AVC Main profile • Non-dyadic decomposition is available

  20. Hierarchical B pictures

  21. Performance

  22. Experiments

  23. Experiments

  24. Conclussion

  25. Conclussion • DCVP & VSP can reduce the correlation between viewpoints • Future work • Depth search algorithms • Motion synthesis • MCTF • Correlation between temporal and viewpoints axis • Rate-control • Scalability • Error protection • Low delay issue

  26. Thanks for your attention!Any questions?

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