Abhik Majumdar, Rohit Puri, Kannan Ramchandran, and Jim Chou /24 1 Distributed Video Coding and Its Application Presented by Lei Sun.

Slides:



Advertisements
Similar presentations
March 24, 2004 Will H.264 Live Up to the Promise of MPEG-4 ? Vide / SURA March Marshall Eubanks Chief Technology Officer.
Advertisements

Introduction to H.264 / AVC Video Coding Standard Multimedia Systems Sharif University of Technology November 2008.
Error detection and concealment for Multimedia Communications Senior Design Fall 06 and Spring 07.
Tomorrow: Uplink Video Transmission Today: Downlink Video Broadcast Changing Landscape of Multimedia Applications.
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,
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.
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
BASiCS Group University of California at Berkeley Generalized Coset Codes for Symmetric/Asymmetric Distributed Source Coding S. Sandeep Pradhan Kannan.
ECE 776 Information Theory Capacity of Fading Channels with Channel Side Information Andrea J. Goldsmith and Pravin P. Varaiya, Professor Name: Dr. Osvaldo.
Efficient Fine Granularity Scalability Using Adaptive Leaky Factor Yunlong Gao and Lap-Pui Chau, Senior Member, IEEE IEEE TRANSACTIONS ON BROADCASTING,
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.
1 Department of Electrical Engineering Stanford University Anne Aaron, Shantanu Rane and Bernd Girod Wyner-Ziv Video Coding with Hash-Based Motion Compensation.
Xinqiao LiuRate constrained conditional replenishment1 Rate-Constrained Conditional Replenishment with Adaptive Change Detection Xinqiao Liu December 8,
1 Department of Electrical Engineering, Stanford University Anne Aaron, Shantanu Rane, Eric Setton and Bernd Girod Transform-domain Wyner-Ziv Codec for.
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.
Why Compress? To reduce the volume of data to be transmitted (text, fax, images) To reduce the bandwidth required for transmission and to reduce storage.
On Error Preserving Encryption Algorithms for Wireless Video Transmission Ali Saman Tosun and Wu-Chi Feng The Ohio State University Department of Computer.
Arko Barman Computer Vision & Artificial Intelligence Lab Department of Electrical Engineering Indian Institute of Science, Bangalore.
Conference title 1 A WYNER-ZIV TO H.264 VIDEO TRANSCODER José Luis Martínez, Pedro Cuenca, Gerardo Fernández-Escribano, Francisco José Quiles and Hari.
Computer Networks Digitization. Spring 2006Computer Networks2 Transfer of an Analog Signal  When analog data (voice, pictures, video) are transformed.
Compression is the reduction in size of data in order to save space or transmission time. And its used just about everywhere. All the images you get on.
1 Secure Cooperative MIMO Communications Under Active Compromised Nodes Liang Hong, McKenzie McNeal III, Wei Chen College of Engineering, Technology, and.
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.
Distributed Source Coding
Adaptive Multi-path Prediction for Error Resilient H.264 Coding Xiaosong Zhou, C.-C. Jay Kuo University of Southern California Multimedia Signal Processing.
Codec structuretMyn1 Codec structure In an MPEG system, the DCT and motion- compensated interframe prediction are combined. The coder subtracts the motion-compensated.
June, 1999 An Introduction to MPEG School of Computer Science, University of Central Florida, VLSI and M-5 Research Group Tao.
Image Compression Supervised By: Mr.Nael Alian Student: Anwaar Ahmed Abu-AlQomboz ID: IT College “Multimedia”
1 Classification of Compression Methods. 2 Data Compression  A means of reducing the size of blocks of data by removing  Unused material: e.g.) silence.
Reed Solomon Code Doug Young Suh Last updated : Aug 1, 2009.
Outline Kinds of Coding Need for Compression Basic Types Taxonomy Performance Metrics.
Compression video overview 演講者:林崇元. Outline Introduction Fundamentals of video compression Picture type Signal quality measure Video encoder and decoder.
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.
Week 7 Lecture 1+2 Digital Communications System Architecture + Signals basics.
DIGITAL COMMUNICATIONS Linear Block Codes
Spring 2000CS 4611 Multimedia Outline Compression RTP Scheduling.
Advances in digital image compression techniques Guojun Lu, Computer Communications, Vol. 16, No. 4, Apr, 1993, pp
Scalable Video Coding and Transport Over Broad-band wireless networks Authors: D. Wu, Y. Hou, and Y.-Q. Zhang Source: Proceedings of the IEEE, Volume:
Entropy Coding of Video Encoded by Compressive Sensing Yen-Ming Mark Lai, University of Maryland, College Park, MD
Dr. Sudharman K. Jayaweera and Amila Kariyapperuma ECE Department University of New Mexico Ankur Sharma Department of ECE Indian Institute of Technology,
Information Theory Linear Block Codes Jalal Al Roumy.
Making Connections Efficient: Multiplexing and Compression Data Communications and Computer Networks: A Business User’s Approach Seventh Edition.
Digital Communications I: Modulation and Coding Course Term Catharina Logothetis Lecture 9.
Flow Control in Compressed Video Communications #2 Multimedia Systems and Standards S2 IF ITTelkom.
C.K. Kim, D.Y. Suh, J. Park, B. Jeon ha 強壯 !. DVC bitstream reorganiser.
(B1) What are the advantages and disadvantages of digital TV systems? Hint: Consider factors on noise, data security, VOD etc. 1.
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.
Image Processing Architecture, © Oleh TretiakPage 1Lecture 5 ECEC 453 Image Processing Architecture Lecture 5, 1/22/2004 Rate-Distortion Theory,
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.
H. 261 Video Compression Techniques 1. H.261  H.261: An earlier digital video compression standard, its principle of MC-based compression is retained.
Introduction to H.264 / AVC Video Coding Standard Multimedia Systems Sharif University of Technology November 2008.
Distributed Compression For Still Images
The Viterbi Decoding Algorithm
Multimedia Outline Compression RTP Scheduling Spring 2000 CS 461.
Image Compression The still image and motion images can be compressed by lossless coding or lossy coding. Principle of compression: - reduce the redundant.
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.
Standards Presentation ECE 8873 – Data Compression and Modeling
MPEG4 Natural Video Coding
Distributed Compression For Binary Symetric Channels
JPEG Still Image Data Compression Standard
Presentation transcript:

Abhik Majumdar, Rohit Puri, Kannan Ramchandran, and Jim Chou /24 1 Distributed Video Coding and Its Application Presented by Lei Sun

Introduction(1/3) /24 2 Contemporary digital video coding architectures have been driven primarily by the “downlink” broadcast model of a complex encoder and multitude of light decoders. However, with the current proliferation of video devices which have constrained computing ability, memory and battery power, we expect future systems to use multiple video input and output streams captured using a network of distributed devices and transmitted over a bandwidth- constrained, noisy wireless transmission medium.

Introduction(2/3) /24 3 System requirements: robustness to packet/frame loss caused by channel transmission errors; low-power and light-footprint encoding due to limited battery power and/or device memory; high compression efficiency due to both bandwidth and transmission power limitation.

Introduction(3/3) /24 4 PRISM (a video coding paradigms founded on the principles of source coding with side information) A flexible distribution of computational complexity between encoder and decoder High compression efficiency

Background on Source Coding with Side Information (1/3) /24 5 Let 3bits binary data X, Y can have the same possibilty of 8 values. they are correlated so the Hamming distance is at most 1. there are 2 scenario showed in figure 1 Scenario a: X can be encoded in 2 bits using (X ⊕ Y) since Y is available both on encoder and decoder. Scenario b: Y is only available on decoder, X encoded in to a coset index so the decoder reception coset index using Y. Figure 1

Background on Source Coding with Side Information (2/3) /24 6 compressing the two or more sources seperately and decoding using the correlation between these sources Slepian and Wolf theorem (lossless case) Wyner-Ziv theorem (lossy case)

Background on Source Coding with Side Information (3/3) /24 7 Figures 2,4 show the structure of the Wyner-Ziv encoding and decoding Figure 2 (a) Encoding consists of quantization followed by a binning operation encoding U into Bin (Coset) index.

(b) Structure of distributed decoders. Decoding consists of “de-binning” followed by estimation. (c) Structure of the codebook bins. Figure 3 /24 8

Architectural Goals of PRISM /24 9 Compression Performance The current macro-block X can be encoded into bin index which reduces the encoding rate. Robustness As long as |Y-X|< δ (step size), the decoder is guaranteed to recover the correct output. Moving Motion-Search Complexity to the Decoder Uncertainty at the receiver about the exactly state of the side information that requires Motion-search at the decoder.

A Theory for Distributed Video Coding /24 10 Sharing Motion Complexity between Encoder and Decoder A Motion-Compensated Video model Figure 4: Motion-indexed additive–innovations model for video signals. X denotes a block of size n pixels in the current frame to be encoded and {Y 1,Y 2 …Y m } is the set of blocks (each of size n) in the previous decoded frame corresponding to different values of the motion vector indexed by T.

Sharing Motion Complexity between Encoder and Decoder… /24 11 Motion-Compensated Predictive Coding Step1:The encoder estimates and transmits the index of the estimated motion vector to the decoder. Step 2: Once the decoder knows T, the video coding problem is reduced to the problem of compressing the “source” X using the correlated side- information Y T now available to both the encoder and the decoder.

Sharing Motion Complexity between Encoder and Decoder… /24 12 Distributed Video Coding In this case, due to severely limited processing capability (or some other reason), the encoder is disallowed from performing the complex motion- compensated prediction task. This is in effect pretending that the encoder does not have access to the previous decoded blocks Y1,...,YM.

A Theory for Distributed Video Coding /24 13 Robustness to Transmission Errors Discrete Data, lossless Recover The R pc lb =H(Z)+H(Y|Y’), In this case, when either channel noise or the accumulated drift is small, the cost of correct errors is not take too many bits, however, if they are big, the rate penalty is significant. Jiontly Gaussian Data, Recovery with MSE<=D In general, if the channel noise is too big, this system is akin to the case of not sending the block at all.

A Theory for Distributed Video Coding /24 14 Complexity Performance Trade-Offs Typically, the more the complexity invested in the motion estimation process, the more accurate is the estimate of the statistics leading to better compression performance.

PRISM: Encoding /24 15 Decorrelating Transform (DCT on source block) Quantization Classification Syndrome Encoding Hash Generation

PRISM: Encoding /24 16 Classification Figure 5: A bit plane view of a block of 64 coefficients. Bit planes are arranged in increasing order with 0 corresponding to the least-significant bit.

Classification… /24 17 depending on the available complexity budget, as well as the prevailing channel conditions, the classification module can perform varying degrees of motion search, ranging from an exhaustive motion search to a coarse motion search to no motion search at all.

PRISM: Encoding /24 18 Hash Generation A hash signature for the quantized sequence codewords is more pratical to let decoder know which is the “best” predictor for the block X.

PRISM: Encoding /24 19 Figure 7: Functional block diagram of the encoder. Figure 6: Bit stream associated with a block.

PRISM: Decoding /24 20 Figure 8: Functional block diagram of the decoder.

Simulation Results /24 21 Figure 9 encoding rate comparison

Simulation Results /24 22 Figure 10 packet drop rate comparison

Simulation Results /24 23 Figure 11 frame Number comparison

Summary /24 24 The PRISM is a pratical video coding framework built on distributed source coding principles. Base on a generalization of the classical Wyner-Ziv step, PRISM is characterized by inherent system uncertain about the “state” of the relevant side information that is know at the decoder. The two main architectural goals of PRISM make it radically different from existing video codecs.