Adaptive delivery of immersive 3D multi-view video over the Internet

The increase in Internet bandwidth and the developments in 3D video technology have paved the way for the delivery of 3D Multi-View Video (MVV) over the Internet. However, large amounts of data and dynamic network conditions result in frequent network congestion, which may prevent video packets from being delivered on time. As a consequence, the 3D video experience may well be degraded unless content-aware precautionary mechanisms and adaptation methods are deployed. In this work, a novel adaptive MVV streaming method is introduced which addresses the future generation 3D immersive MVV experiences with multi-view displays. When the user experiences network congestion, making it necessary to perform adaptation, the rate-distortion optimum set of views that are pre-determined by the server, are truncated from the delivered MVV streams. In order to maintain high Quality of Experience (QoE) service during the frequent network congestion, the proposed method involves the calculation of low-overhead additional metadata that is delivered to the client. The proposed adaptive 3D MVV streaming solution is tested using the MPEG Dynamic Adaptive Streaming over HTTP (MPEG-DASH) standard. Both extensive objective and subjective evaluations are presented, showing that the proposed method provides significant quality enhancement under the adverse network conditions.

[1]  Nicola Blefari-Melazzi,et al.  Mobile peer-to-peer video streaming over information-centric networks , 2015, Comput. Networks.

[2]  Yo-Sung Ho,et al.  Hole filling method using depth based in-painting for view synthesis in free viewpoint television and 3-D video , 2009, 2009 Picture Coding Symposium.

[3]  Lu Yu,et al.  A perceptual metric for evaluating quality of synthesized sequences in 3DV system , 2010, Visual Communications and Image Processing.

[4]  Yasuo Sugiyama,et al.  An algorithm for solving discrete-time Wiener-Hopf equations based upon Euclid's algorithm , 1986, IEEE Trans. Inf. Theory.

[5]  Alfred C. Weaver,et al.  On Retransmission-Based Error Control for Continuous Media Traffic in Packet-Switching Networks , 1996, Comput. Networks ISDN Syst..

[6]  Frederic Dufaux,et al.  Emerging Technologies for 3D Video: Creation, Coding, Transmission and Rendering , 2013, Emerging Technologies for 3D Video.

[7]  D.M. Mount,et al.  An Efficient k-Means Clustering Algorithm: Analysis and Implementation , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[8]  Gary J. Sullivan,et al.  Overview of the High Efficiency Video Coding (HEVC) Standard , 2012, IEEE Transactions on Circuits and Systems for Video Technology.

[9]  Wojciech Matusik,et al.  3D TV: a scalable system for real-time acquisition, transmission, and autostereoscopic display of dynamic scenes , 2004, ACM Trans. Graph..

[10]  Yaning Liu,et al.  Dynamic adaptive streaming over CCN: A caching and overhead analysis , 2013, 2013 IEEE International Conference on Communications (ICC).

[11]  Martin Reisslein,et al.  The rate variability-distortion (VD) curve of encoded video and its impact on statistical multiplexing , 2005, IEEE Transactions on Broadcasting.

[12]  Tao Chen,et al.  3D-TV Content Storage and Transmission , 2011, IEEE Transactions on Broadcasting.

[13]  Jacob Chakareski Adaptive multiview video streaming: challenges and opportunities , 2013, IEEE Communications Magazine.

[14]  ITU-T Rec. P.910 (04/2008) Subjective video quality assessment methods for multimedia applications , 2009 .

[15]  Thomas Stockhammer,et al.  Dynamic adaptive streaming over HTTP --: standards and design principles , 2011, MMSys.

[16]  Gary J. Sullivan,et al.  Overview of the Stereo and Multiview Video Coding Extensions of the H.264/MPEG-4 AVC Standard , 2011, Proceedings of the IEEE.

[17]  Christian Timmerer,et al.  An evaluation of dynamic adaptive streaming over HTTP in vehicular environments , 2012, MoVid '12.

[18]  Heiko Schwarz,et al.  3D video coding using the synthesized view distortion change , 2012, 2012 Picture Coding Symposium.

[19]  Van Jacobson,et al.  Networking named content , 2009, CoNEXT '09.

[20]  Zhaozheng Yin,et al.  Improving depth perception with motion parallax and its application in teleconferencing , 2009, 2009 IEEE International Workshop on Multimedia Signal Processing.

[21]  Ajay Luthra,et al.  Overview of the H.264/AVC video coding standard , 2003, SPIE Optics + Photonics.

[22]  Anh T. Pham,et al.  Adaptive streaming of audiovisual content using MPEG DASH , 2012, IEEE Transactions on Consumer Electronics.

[23]  Luigi Rizzo,et al.  Dummynet: a simple approach to the evaluation of network protocols , 1997, CCRV.

[24]  Antonio Ortega,et al.  Depth map distortion analysis for view rendering and depth coding , 2009, 2009 16th IEEE International Conference on Image Processing (ICIP).

[25]  T. Wiegand,et al.  Block based Rate-Distortion analysis for quality improvement of synthesized views , 2010, 2010 3DTV-Conference: The True Vision - Capture, Transmission and Display of 3D Video.

[26]  Ahmet M. Kondoz,et al.  3D Future Internet Media , 2013 .

[27]  A. Murat Tekalp,et al.  Adaptive stereoscopic 3D video streaming , 2010, 2010 IEEE International Conference on Image Processing.

[28]  Ying Chen,et al.  Standardized Extensions of High Efficiency Video Coding (HEVC) , 2013, IEEE Journal of Selected Topics in Signal Processing.

[29]  Gary J. Sullivan,et al.  Rate-distortion optimization for video compression , 1998, IEEE Signal Process. Mag..

[30]  Gary J. Sullivan,et al.  Efficient quadtree coding of images and video , 1994, IEEE Trans. Image Process..

[31]  Van Jacobson,et al.  Congestion avoidance and control , 1988, SIGCOMM '88.

[32]  Thomas Wiegand,et al.  Temporally consistent handling of disocclusions with texture synthesis for depth-image-based rendering , 2010, 2010 IEEE International Conference on Image Processing.

[33]  A. Murat Tekalp,et al.  Adaptive Streaming of Multiview Video Over P2P Networks , 2013, Emerging Technologies for 3D Video.

[34]  Krzysztof Wegner,et al.  High Efficiency 3D Video Coding Using New Tools Based on View Synthesis , 2013, IEEE Transactions on Image Processing.

[35]  Ismo Rakkolainen,et al.  A Survey of 3DTV Displays: Techniques and Technologies , 2007, IEEE Transactions on Circuits and Systems for Video Technology.

[36]  I. Melzer Web Services Description Language , 2010 .

[37]  Antonio Ortega,et al.  On Dependent Bit Allocation for Multiview Image Coding With Depth-Image-Based Rendering , 2011, IEEE Transactions on Image Processing.

[38]  G. Bjontegaard,et al.  Calculation of Average PSNR Differences between RD-curves , 2001 .

[39]  Ajay Luthra,et al.  Overview of the H.264/AVC video coding standard , 2003, IEEE Trans. Circuits Syst. Video Technol..

[40]  Bruce M. Maggs,et al.  An analysis of live streaming workloads on the internet , 2004, IMC '04.

[41]  Detlev Marpe,et al.  Block Merging for Quadtree-Based Partitioning in HEVC , 2012, IEEE Transactions on Circuits and Systems for Video Technology.

[42]  Ahmet M. Kondoz,et al.  Dynamic adaptive 3D multi-view video streaming over the internet , 2013, ImmersiveMe '13.

[43]  Sugato Chakravarty,et al.  Methodology for the subjective assessment of the quality of television pictures , 1995 .

[44]  Christian Timmerer,et al.  Dynamic adaptive streaming over HTTP dataset , 2012, MMSys '12.

[45]  Ahmet M. Kondoz,et al.  Adaptive 3D multi-view video streaming over P2P networks , 2014, 2014 IEEE International Conference on Image Processing (ICIP).

[46]  Iraj Sodagar,et al.  The MPEG-DASH Standard for Multimedia Streaming Over the Internet , 2011, IEEE MultiMedia.

[47]  Martin Reisslein,et al.  WVSNP-DASH: Name-Based Segmented Video Streaming , 2015, IEEE Transactions on Broadcasting.

[48]  Eero P. Simoncelli,et al.  Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.

[49]  A. Murat Tekalp,et al.  Evaluation of adaptation methods for multi-view video , 2012, 2012 19th IEEE International Conference on Image Processing.

[50]  Sanjit K. Mitra,et al.  Quadtree Optimization for Image and Video Coding , 1997, J. VLSI Signal Process..

[51]  Victor C. M. Leung,et al.  A rate adaptation approach for streaming multiview plus depth content , 2014, 2014 International Conference on Computing, Networking and Communications (ICNC).

[52]  T. Grajek,et al.  Subjective quality assessment methodology for 3D video compression technology , 2012, 2012 International Conference on Signals and Electronic Systems (ICSES).

[53]  Dong Tian,et al.  On modeling the rendering error in 3D video , 2012, 2012 19th IEEE International Conference on Image Processing.

[54]  Christoph Fehn,et al.  Depth-image-based rendering (DIBR), compression, and transmission for a new approach on 3D-TV , 2004, IS&T/SPIE Electronic Imaging.

[55]  Shang-Hong Lai,et al.  Spatio-Temporally Consistent Novel View Synthesis Algorithm From Video-Plus-Depth Sequences for Autostereoscopic Displays , 2011, IEEE Transactions on Broadcasting.

[56]  Masayuki Tanimoto Overview of FTV (free-viewpoint television) , 2009, 2009 IEEE International Conference on Multimedia and Expo.

[57]  Aljoscha Smolic,et al.  Coding Algorithms for 3DTV—A Survey , 2007, IEEE Transactions on Circuits and Systems for Video Technology.

[58]  Adam Wolisz,et al.  Adaptation algorithm for adaptive streaming over HTTP , 2012, 2012 19th International Packet Video Workshop (PV).

[59]  Martin Reisslein,et al.  Traffic and Statistical Multiplexing Characterization of 3-D Video Representation Formats , 2013, IEEE Transactions on Broadcasting.

[60]  S. Cho,et al.  Adaptive Local Illumination Change Compensation Method for H.264/AVC-Based Multiview Video Coding , 2007, IEEE Transactions on Circuits and Systems for Video Technology.

[61]  Ahmet M. Kondoz,et al.  Adaptive streaming of multi-view video over P2P networks , 2012, Signal Process. Image Commun..

[62]  B. Cohen,et al.  Incentives Build Robustness in Bit-Torrent , 2003 .

[63]  Ahmet M. Kondoz,et al.  Digital Speech: Coding for Low Bit Rate Communication Systems , 1995 .

[64]  Levent Onural,et al.  Television in 3-D: What Are the Prospects? , 2007 .

[65]  Heiko Schwarz,et al.  3D High-Efficiency Video Coding for Multi-View Video and Depth Data , 2013, IEEE Transactions on Image Processing.

[66]  Oscar C. Au,et al.  Rate-distortion optimized 3D reconstruction from noise-corrupted multiview depth videos , 2013, 2013 IEEE International Conference on Multimedia and Expo (ICME).

[67]  A Survey of Peer-to-Peer Networks , 2005 .

[68]  Liang Tian,et al.  Rendering Distortion Estimation Model for 3D High Efficiency Depth Coding , 2014 .