Wednesday, Jan 21, 1:30 to 3:10 pm, Session 15 : Image/Video Transmission I (First Talk, Other topics deal with error-resilience and error-concealment)

Slides:



Advertisements
Similar presentations
Video Coding For Compression... and Beyond Bernd Girod Information Systems Laboratory Department of Electrical Engineering Stanford University.
Advertisements

Introduction to H.264 / AVC Video Coding Standard Multimedia Systems Sharif University of Technology November 2008.
2005/01/191/14 Overview of Fine Granularity Scalability in MPEG-4 Video Standard Weiping Li Fellow, IEEE IEEE Transactions on Circuits and Systems for.
Forward Error Correction Demystified Presented by Sunrise Telecom Broadband … a step ahead.
Digital Fountain Codes V. S
1 Distributed Source Coding Trial Lecture Fredrik Hekland 1. June 2007.
Limin Liu, Member, IEEE Zhen Li, Member, IEEE Edward J. Delp, Fellow, IEEE CSVT 2009.
Recursive End-to-end Distortion Estimation with Model-based Cross-correlation Approximation Hua Yang, Kenneth Rose Signal Compression Lab University of.
1 Department of Electrical Engineering, Stanford University Anne Aaron, Shantanu Rane, David Rebollo-Monedero and Bernd Girod Systematic Lossy Forward.
Overview of Error Resiliency Schemes in H.264/AVC Standard Sunil Kumar, Liyang Xu, Mrinal K. Mandal, and Sethuraman Panchanathan Elsevier Journal of Visual.
An Error-Resilient GOP Structure for Robust Video Transmission Tao Fang, Lap-Pui Chau Electrical and Electronic Engineering, Nanyan Techonological University.
Reinventing Compression: The New Paradigm of Distributed Video Coding Bernd Girod Information Systems Laboratory Stanford University.
Multiple Description Coding and Distributed Source Coding: Unexplored Connections in Information Theory and Coding Theory S. Sandeep Pradhan Department.
Rate-Distortion Optimized Layered Coding with Unequal Error Protection for Robust Internet Video Michael Gallant, Member, IEEE, and Faouzi Kossentini,
Robust Scalable Video Streaming over Internet with Network-Adaptive Congestion Control and Unequal Loss Protection Quan Zang, Guijin Wang, Wenwu Zhu, and.
Motion-compensation Fine-Granular-Scalability (MC-FGS) for wireless multimedia M. van der Schaar, H. Radha Proceedings of IEEE Symposium on Multimedia.
Distributed Video Coding Bernd Girod, Anne Margot Aagon and Shantanu Rane, Proceedings of IEEE, Jan, 2005 Presented by Peter.
Wyner-Ziv Coding of Motion Video
Encoder and Decoder Optimization for Source-Channel Prediction in Error Resilient Video Transmission Hua Yang and Kenneth Rose Signal Compression Lab ECE.
Error Concealment For Fine Granularity Scalable Video Transmission Hua Cai; Guobin Shen; Feng Wu; Shipeng Li; Bing Zeng; Multimedia and Expo, Proceedings.
Efficient Fine Granularity Scalability Using Adaptive Leaky Factor Yunlong Gao and Lap-Pui Chau, Senior Member, IEEE IEEE TRANSACTIONS ON BROADCASTING,
Video Streaming: An FEC-Based Novel Approach Jianfei Cai, Chang Wen Chen Electrical and Computer Engineering, Canadian Conference on.
Error Resilience in a Generic Compressed Video Stream Transmitted over a Wireless Channel Muhammad Bilal
Wireless FGS video transmission using adaptive mode selection and unequal error protection Jianhua Wu and Jianfei Cai Nanyang Technological University.
Wyner-Ziv Residual Coding of Video Anne Aaron, David Varodayan and Bernd Girod Information Systems Laboratory Stanford University.
Seamless Switching of Scalable Video Bitstreams for Efficient Streaming Xiaoyan Sun, Feng Wu, Shipeng Li, Wen, Gao, and Ya-Qin Zhang.
Source-Channel Prediction in Error Resilient Video Coding Hua Yang and Kenneth Rose Signal Compression Laboratory ECE Department University of California,
1 Department of Electrical Engineering Stanford University Anne Aaron, Shantanu Rane and Bernd Girod Wyner-Ziv Video Coding with Hash-Based Motion Compensation.
` 1 Department of Electrical Engineering, Stanford University Anne Aaron, Prashant Ramanathan and Bernd Girod Wyner-Ziv Coding of Light Fields for Random.
Multi-Path Transport of FGS Video Jian Zhou, Huai-Rong Shao, Chia Shen and Ming-Ting Sun ICME 2003.
H.264/AVC for Wireless Applications Thomas Stockhammer, and Thomas Wiegand Institute for Communications Engineering, Munich University of Technology, Germany.
4/24/2002SCL UCSB1 Optimal End-to-end Distortion Estimation for Drift Management in Scalable Video Coding H. Yang, R. Zhang and K. Rose Signal Compression.
1 Department of Electrical Engineering, Stanford University Anne Aaron, Shantanu Rane, Eric Setton and Bernd Girod Transform-domain Wyner-Ziv Codec for.
Unequal Loss Protection: Graceful Degradation of Image Quality over Packet Erasure Channels Through Forward Error Correction Alexander E. Mohr, Eva A.
Distributed Video Coding Bernd Girod, Anne Margot Aaron, Shantanu Rane, and David Rebollo-Monedero IEEE Proceedings 2005.
Distributed Video Coding VLBV, Sardinia, September 16, 2005 Bernd Girod Information Systems Laboratory Stanford University.
09/24/02ICIP20021 Drift Management and Adaptive Bit Rate Allocation in Scalable Video Coding H. Yang, R. Zhang and K. Rose Signal Compression Lab ECE Department.
MPEG MPEG-VideoThis deals with the compression of video signals to about 1.5 Mbits/s; MPEG-AudioThis deals with the compression of digital audio signals.
ON DATACASTING OF H.264/AVC OVER DVB-H Multimedia Signal Processing, 2005 IEEE 7th Workshop on Publication Date: Oct Nov Reporter: 陳志明.
Abhik Majumdar, Rohit Puri, Kannan Ramchandran, and Jim Chou /24 1 Distributed Video Coding and Its Application Presented by Lei Sun.
Distributed Source Coding
A Robust Resolution-Enhancement Scheme for Video Transmission Over Mobile Ad-Hoc Networks Authors : Source : IEEE TRANSACTIONS ON BROADCASTING, VOL. 54,
Adaptive Multi-path Prediction for Error Resilient H.264 Coding Xiaosong Zhou, C.-C. Jay Kuo University of Southern California Multimedia Signal Processing.
- By Naveen Siddaraju - Under the guidance of Dr K R Rao Study and comparison of H.264/MPEG4.
- By Naveen Siddaraju - Under the guidance of Dr K R Rao Study and comparison between H.264.
Rate-distortion Optimized Mode Selection Based on Multi-channel Realizations Markus Gärtner Davide Bertozzi Classroom Presentation 13 th March 2001.
A New Coding Mode for Error Resilient Video EE368C Final Presentation Stanford University Sangoh Jeong Mar.8, 2001.
Brief Overview of Wyner-Ziv CODEC and Research Plan Jin-soo KIM.
Wyner-Ziv Coding of Motion Video Presented by fakewen.
Overview of Fine Granularity Scalability in MPEG-4 Video Standard Weiping Li Presented by : Brian Eriksson.
Video Compression—From Concepts to the H.264/AVC Standard
C.K. Kim, D.Y. Suh, J. Park, B. Jeon ha 強壯 !. DVC bitstream reorganiser.
Blind Quality Assessment System for Multimedia Communications Using Tracing Watermarking P. Campisi, M. Carli, G. Giunta and A. Neri IEEE Transactions.
Fundamentals of Multimedia Chapter 17 Wireless Networks 건국대학교 인터넷미디어공학부 임 창 훈.
1 Department of Electrical Engineering, Stanford University Anne Aaron, Shantanu Rane, Rui Zhang and Bernd Girod Wyner-Ziv Coding for Video: Applications.
1 Department of Electrical Engineering, Stanford University EE 392J Final Project Presentation Shantanu Rane Hash-Aided Motion Estimation & Rate Control.
Introduction to H.264 / AVC Video Coding Standard Multimedia Systems Sharif University of Technology November 2008.
Overview of the Scalable Video Coding
BITS Pilani Pilani Campus EEE G612 Coding Theory and Practice SONU BALIYAN 2017H P.
Data Compression.
2018/9/16 Distributed Source Coding Using Syndromes (DISCUS): Design and Construction S.Sandeep Pradhan, Kannan Ramchandran IEEE Transactions on Information.
Limitations of Traditional Error-Resilience Methods
Huahui Wu, Mark Claypool, Robert Kinicki Computer Science,
Wyner-Ziv Coding of Video - Towards Practical Distributed Coding -
Scalable Speech Coding for IP Networks: Beyond iLBC
Standards Presentation ECE 8873 – Data Compression and Modeling
Shantanu Kulkarni UTA ID:
Unequal Error Protection for Video Transmission over Wireless Channels
強壯的進度 2011/12/28 我是強壯XD.
Presentation transcript:

Wednesday, Jan 21, 1:30 to 3:10 pm, Session 15 : Image/Video Transmission I (First Talk, Other topics deal with error-resilience and error-concealment)

Limitations of traditional error-resilience methods Broadcasting+FEC+PET+FEP FEC (“Cliff” effect) Layered Coding with Priority Encoding Transmission (PET) (Inferior R-D Performance) [Albanese et al., 1996]

Outline Systematic source-channel coding framework Lossy Forward Error Protection using Wyner-Ziv coding Results and Conclusions The audience will need a little intro on WZ coding. No equations, just the idea.

Wyner-Ziv coding background Side-info at encoder and decoder: encoder decoder X X’ Y encoder decoder X X’ Y Side-info at decoder only: [Wyner and Ziv, 1975-76] Can achieve bit-rate savings due to correlation between X and Y

Systematic Source-Channel Coding Wyner-Ziv Encoder Side info Digital Channel Analog Decoder [Shamai, Verdu and Zamir, 1998] Enhancing analog transmission systems using digital side information [Pradhan and Ramchandran, 2001] Robust predictive coding [Sehgal and Ahuja, 2003] Lossy source-channel coding of video waveforms [Aaron, Rane and Girod, 2003] Channels A and D+WZ relation+Pradhan+Sehgal

MPEG Decoder with Error Concealment Systematic lossy forward error protection MPEG Encoder MPEG Decoder with Error Concealment S S’ Side information Error-Prone channel Slepian-Wolf Encoder Slepian-Wolf Decoder Reconstruction Coarse Quantizer S* Wyner-Ziv Encoder Wyner-Ziv Decoder Conventional encoded+decoded+WZ representation (explain later)+decoding+lossy (clear later) Systematic source-channel coding “Lossy” protection Fully backward compatible with legacy systems

Fallback Scheme for error-resilience T T-1 Q-1 Q MC EC Main Encoder - S + Channel ED T-1 Q-1 + MC S* + T T-1 q q-1 MC EC Fallback Encoder - T-1 q-1 ED + MC Fallback when errors occur say lossy

Proposed Wyner-Ziv codec Video Encoder main S Channel ED T-1 Q-1 + MC S* T-1 q-1 ED + MC Side information Video Encoder “coarse” Reconstructed frame at Encoder Video “coarse” R-S Decoder R-S Encoder Conventional+fallback+generate side info+send only parity+reason for lossy Transmit only parity symbols Wyner-Ziv encoder Wyner-Ziv decoder

Transmit along this direction Reed-Solomon codes across slices Transmit along this direction k n X X row/block+filler+n,k explanation+erasure+anim play RS code across slices 1 byte in slice filler byte Erasure Decoding parity byte

Simulation setup Codecs: Main Codec  H.26L (JM2.0) codec WZ Codec  H.26L codec and R-S codec. Settings: 1 Slice = 11 macroblocks = 1/2 GOB for CIF frame Identical slice structure for main and WZ stream

Results (1) Foreman.CIF Main stream @ 1.092 Mbps FEC (n,k) = (40,36) FEC bitrate = 120 Kbps Total = 1.2 Mbps Coarse stream @ 270 Kbps FEP (n,k) = (52,36) WZ bitrate = 120 Kbps Foreman bitrate+FEC bitrate+FEP bitrate+larger range+error concealment versus quantization

Results (2) Visual Comparison Foreman 50 CIF frames @ symbol error rate = 4 x 10-4 With FEC 1.092 Mbps + 120 kbps (38.32 dB) With FEP 1.092 Mbps + 120 kbps (38.78 dB)

Results (3) Visual Comparison Foreman 50 CIF frames @ symbol error rate = 10-3 Quantization artifacts invisible With FEC 1.092 Mbps + 120 kbps (33.03 dB) With FEP 1.092 Mbps + 120 kbps (38.40 dB)

Results (2) Coastguard.CIF Main stream @ 3.175 Mbps FEC (n,k) = (40,36) FEC bitrate = 352.78 Kbps Total = 3.5 Mbps Coarse stream @ 1 Mbps FEP (n.k) = (44,36) WZ bitrate = 220 Kbps Total = 3.4 Mbps Coarse stream @ 658 Kbps FEP (n.k) = (48,36) Add the third curve here, as presented in the paper (Important).

MPEG Decoder with Error Concealment Ongoing Work : Embedded WZ codec MPEG Encoder MPEG Decoder with Error Concealment S S’ S* Wyner-Ziv Encoder A Decoder A S** Wyner-Ziv Encoder B Decoder B Error-Prone channel Graceful degradation of video quality Does not require layered representation of original video signal

Conclusions Systematic lossy forward error protection scheme for error- resilient digital video broadcasting Outperforms conventional FEC schemes, when SER increases Fully backward compatible with legacy broadcast systems Can construct embedded Wyner-Ziv codec which achieves graceful degradation without layered representation.