1 Department of Electrical Engineering, Stanford University Anne Aaron, Shantanu Rane, Rui Zhang and Bernd Girod Wyner-Ziv Coding for Video: Applications.

Slides:



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

1 Distributed Source Coding Trial Lecture Fredrik Hekland 1. June 2007.
Tomorrow: Uplink Video Transmission Today: Downlink Video Broadcast Changing Landscape of Multimedia Applications.
Advances in Network-adaptive Video Streaming Bernd Girod J. Chakareski, M. Kalman, Y. J. Liang, E. Setton, R. Zhang Information Systems Laboratory Department.
Limin Liu, Member, IEEE Zhen Li, Member, IEEE Edward J. Delp, Fellow, IEEE CSVT 2009.
SCHOOL OF COMPUTING SCIENCE SIMON FRASER UNIVERSITY CMPT 820 : Error Mitigation Schaar and Chou, Multimedia over IP and Wireless Networks: Compression,
1 Department of Electrical Engineering, Stanford University Anne Aaron, Shantanu Rane, David Rebollo-Monedero and Bernd Girod Systematic Lossy Forward.
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,
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
Losslessy Compression of Multimedia Data Hao Jiang Computer Science Department Sept. 25, 2007.
Encoder and Decoder Optimization for Source-Channel Prediction in Error Resilient Video Transmission Hua Yang and Kenneth Rose Signal Compression Lab ECE.
School of Computing Science Simon Fraser University
BASiCS Group University of California at Berkeley Generalized Coset Codes for Symmetric/Asymmetric Distributed Source Coding S. Sandeep Pradhan Kannan.
Bernd Girod: Image Compression and Graphics 1 Image Compression and Graphics: More Than a Sum of Parts? Bernd Girod Collaborators: Peter Eisert, Marcus.
Error Resilience in a Generic Compressed Video Stream Transmitted over a Wireless Channel Muhammad Bilal
Transform Domain Distributed Video Coding. Outline  Another Approach  Side Information  Motion Compensation.
Wyner-Ziv Residual Coding of Video Anne Aaron, David Varodayan and Bernd Girod Information Systems Laboratory Stanford University.
Investigation of Motion-Compensated Lifted Wavelet Transforms Information Systems Laboratory Department of Electrical Engineering Stanford University Markus.
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.
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.
Compression with Side Information using Turbo Codes Anne Aaron and Bernd Girod Information Systems Laboratory Stanford University Data Compression Conference.
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.
Linear Codes for Distributed Source Coding: Reconstruction of a Function of the Sources -D. Krithivasan and S. Sandeep Pradhan -University of Michigan,
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.
Arko Barman Computer Vision & Artificial Intelligence Lab Department of Electrical Engineering Indian Institute of Science, Bangalore.
Electrical Engineering National Central University Video-Audio Processing Laboratory Data Error in (Networked) Video M.K.Tsai 04 / 08 / 2003.
Video Compression Techniques By David Ridgway.
Introduction Compression Performance Conclusions Large Camera Arrays Capture multi-viewpoint images of a scene/object. Potential applications abound: surveillance,
Li-Wei Kang ( 康立威 ) Institute of Information Science, Academia Sinica Taipei, Taiwan 中央研究院資訊科學研究所 博士後研究員 Feb. 22, 2008 Distributed.
Error control in video Streaming. Introduction Development of different types of n/ws such as internet, wireless and mobile networks has created new applications.
Videos Mei-Chen Yeh. Outline Video representation Basic video compression concepts – Motion estimation and compensation Some slides are modified from.
Abhik Majumdar, Rohit Puri, Kannan Ramchandran, and Jim Chou /24 1 Distributed Video Coding and Its Application Presented by Lei Sun.
Distributed Source Coding
FRE /09/2005 Distributed Source Coding of Still Images Ç. Dikici,R. Guermazi, K. Idrissi, A.Baskurt LIRIS, UMR 5205 CNRS INSA de Lyon, France.
Adaptive Multi-path Prediction for Error Resilient H.264 Coding Xiaosong Zhou, C.-C. Jay Kuo University of Southern California Multimedia Signal Processing.
Statistical Characteristics of Simple Wyner-Ziv Frames Jin-soo KIM.
- By Naveen Siddaraju - Under the guidance of Dr K R Rao Study and comparison of H.264/MPEG4.
MPEG MPEG : Motion Pictures Experts Group MPEG : ISO Committee Widely Used Video Compression Standard.
Progressive Side Information Refinement with Non-Local Means Denoising in Distributed Video Coding 使用於分散式視訊編碼之非區域平均去雜訊循 序旁資訊改善技術 Wang, Pin-Hsiang 王品翔 Advisor:
Sub pixel motion estimation for Wyner-Ziv side information generation Subrahmanya M V (Under the guidance of Dr. Rao and Dr.Jin-soo Kim)
- 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.
Brief Overview of Wyner-Ziv CODEC and Research Plan Jin-soo KIM.
Compression of Real-Time Cardiac MRI Video Sequences EE 368B Final Project December 8, 2000 Neal K. Bangerter and Julie C. Sabataitis.
New Direction in Wyner-Ziv Video Coding: On the Importance of Modeling Virtual Correlation Channel (VCC) Xin Li LDCSEE, WVU “ If.
Fast motion estimation and mode decision for H.264 video coding in packet loss environment Li Liu, Xinhua Zhuang Computer Science Department, University.
Wyner-Ziv Coding of Motion Video Presented by fakewen.
Technion- Israel Institute of Technology Faculty of Electrical Engineering CCIT-Computer Network Laboratory The Influence of Packet Loss On Video Quality.
C.K. Kim, D.Y. Suh, J. Park, B. Jeon ha 強壯 !. DVC bitstream reorganiser.
Li-Wei Kang and Chun-Shien Lu Institute of Information Science, Academia Sinica Taipei, Taiwan, ROC {lwkang, April IEEE.
Samuel Cheng, Shuang Wang and Lijuan Cui University of Oklahoma
1 Department of Electrical Engineering, Stanford University EE 392J Final Project Presentation Shantanu Rane Hash-Aided Motion Estimation & Rate Control.
Principles of Video Compression Dr. S. M. N. Arosha Senanayake, Senior Member/IEEE Associate Professor in Artificial Intelligence Room No: M2.06
Distributed Video System realized on mobile device with efficient Feedback channel 分散式影像編碼在手機上的實現與有效率 的回饋通道 1 Chen,chun-yuan 陳群元 Advisor:Prof. Wu,Ja-Ling.
BITS Pilani Pilani Campus EEE G612 Coding Theory and Practice SONU BALIYAN 2017H P.
2018/9/16 Distributed Source Coding Using Syndromes (DISCUS): Design and Construction S.Sandeep Pradhan, Kannan Ramchandran IEEE Transactions on Information.
Streaming To Mobile Users In A Peer-to-Peer Network
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
Wyner-Ziv Coding of Video - Towards Practical Distributed Coding -
Standards Presentation ECE 8873 – Data Compression and Modeling
Presentation transcript:

1 Department of Electrical Engineering, Stanford University Anne Aaron, Shantanu Rane, Rui Zhang and Bernd Girod Wyner-Ziv Coding for Video: Applications to Compression and Error Resilience

Wyner-Ziv Coding for Video: Applications to Compression and Error Resilience March 25, Overview  Distributed Source Coding  Intraframe Encoding with Interframe Decoding  Systematic Lossy Forward Error Protection

Wyner-Ziv Coding for Video: Applications to Compression and Error Resilience March 25, Distributed Source Coding Encoder Decoder Statistically dependent Slepian-Wolf Theorem Encoder Decoder Wyner-Ziv Theorem Statistically dependent

Wyner-Ziv Coding for Video: Applications to Compression and Error Resilience March 25, Practical Distributed Source Coding Practical Codes  Coset encoding [Pradhan and Ramchandran, 1999]  Trellis codes [Wang and Orchard, 2001]  Turbo codes [Garcia-Frias and Zhao, 2001] [Bajcsy and Mitran, 2001] [Aaron and Girod, 2002]  LDPC codes [Liveris, Xiong, and Georghiades, 2002] Applications  Image and Video [Pradhan and Ramchandran, 2001] [Liveris, Xiong, and Georghiades, 2002] [Jagmohan, Sehgal, and Ahuja, 2002] [Puri and Ramchandran, 2002] [Aaron, Zhang and Girod, 2002]  Sensor Networks [Chou, Petrovic and Ramchandran, 2002]

Wyner-Ziv Coding for Video: Applications to Compression and Error Resilience March 25, Wyner-Ziv Video Codec Wyner-Ziv Decoder Scalar Quantizer X Wyner-Ziv Encoder Reconstruction X’ Y Turbo Encoder Turbo Decoder Slepian-Wolf Codec

Wyner-Ziv Coding for Video: Applications to Compression and Error Resilience March 25, Wyner-Ziv Coding for Compression Interframe Decoder Intraframe Encoder XiXi X i-1 ’Xi’Xi’ Wyner-Ziv Coding Side Information Compression for mobile video cameras  Simple encoder  Possibly complex decoder

Wyner-Ziv Coding for Video: Applications to Compression and Error Resilience March 25, Intraframe Encoder - Interframe Decoder Reconstruction X’ Y Interframe Decoder Scalar Quantizer Turbo Encoder Buffer Even frame X Intraframe Encoder Turbo Decoder Request bits Slepian-Wolf Codec Interpolation Odd frames previous next Limits reconstruction distortion based on quantizer coarseness Very simple encoder Turbo code can perform joint source-channel decoding Decoder controls rate and generates side information

Wyner-Ziv Coding for Video: Applications to Compression and Error Resilience March 25, Rate-PSNR Plots compared to H dB 4 dB 7 dB Foreman QCIF sequence Uniform {2, 4, 16} level quantizers Slepian-Wolf codec  Rate 4/5 Turbo code  P e <10 -3 ~ 25 pixels per frame Interpolation – MC with symmetric motion vectors

Wyner-Ziv Coding for Video: Applications to Compression and Error Resilience March 25, Rate-PSNR Plots compared to H.263+ Carphone QCIF sequence Uniform {2, 4, 16} level quantizers Slepian-Wolf codec  Rate 4/5 Turbo code  P e <10 -3 ~ 25 pixels per frame Interpolation – MC with symmetric motion vectors 6 dB 2 dB 8 dB

Wyner-Ziv Coding for Video: Applications to Compression and Error Resilience March 25, Foreman sequence Side information After Wyner-Ziv Coding 16-level quantization (~1 bpp)

Wyner-Ziv Coding for Video: Applications to Compression and Error Resilience March 25, Sample Frame (Foreman) Side information After Wyner-Ziv Coding 16-level quantization (~1 bpp)

Wyner-Ziv Coding for Video: Applications to Compression and Error Resilience March 25, Carphone Sequence H263+ Intraframe Coding 410 kbps Wyner-Ziv Codec 384 kbps

Wyner-Ziv Coding for Video: Applications to Compression and Error Resilience March 25, Wyner-Ziv Coding for Error Resilience Conventional Forward Error Correction (FEC)  Protects the bit stream representing the video signal  “Lossless” correction  For graceful degradation, needs layered representation of video Systematic Lossy Forward Error Protection

Wyner-Ziv Coding for Video: Applications to Compression and Error Resilience March 25, Systematic Lossy Forward Error Protection MPEG Encoder MPEG Decoder with Error Concealment Error-Prone channel S S’ Wyner-Ziv Decoder Scalar Quantizer Wyner-Ziv Encoder Reconstruction Turbo Encoder Turbo Decoder S*S* Protects the original video waveform “Lossy” protection For graceful degradation, does not require layered representation of video

Wyner-Ziv Coding for Video: Applications to Compression and Error Resilience March 25, Results Carphone CIF Sequence H.26L encoding at 1 Mbps 1% macroblock loss Error-free Wyner-Ziv bits 4 and 16 level quantization Rate 4/5 turbo code P e <10 -3 ~ 100 pixels per frame

Wyner-Ziv Coding for Video: Applications to Compression and Error Resilience March 25, Carphone Sequence No Error Protection 1% macroblock loss 33 dB With forward error protection of 1.5 bpp 1% macroblock loss 38 dB

Wyner-Ziv Coding for Video: Applications to Compression and Error Resilience March 25, Embedded Wyner-Ziv Codec MPEG Encoder MPEG Decoder with Error Concealment Error-Prone channel S S’ Wyner-Ziv Decoder A S*S* Wyner-Ziv Encoder A Wyner-Ziv Decoder B S ** Wyner-Ziv Encoder B Graceful degradation Does not require layered representation

Wyner-Ziv Coding for Video: Applications to Compression and Error Resilience March 25, Conclusions Wyner-Ziv coding for two video applications Intraframe encoder-Interframe decoder  Very simple encoder  Performs up to dB better than H.263+ intraframe coding Systematic Lossy Forward Error Protection  Protects the video waveform  Backward compatible  Can achieve graceful degradation without layered representation