Video Modeling Pravin Rajamoney CSE-581 Network Technology.

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Video Modeling Pravin Rajamoney CSE-581 Network Technology

Transcript of Video Modeling Pravin Rajamoney CSE-581 Network Technology.

Page 1: Video Modeling Pravin Rajamoney CSE-581 Network Technology.

Video Modeling

Pravin Rajamoney

CSE-581 Network Technology

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Papers:

• Analysis, Modeling and Generation of Self-Similar VBR Video Traffic. M.W.Garrentt and W.Willinger

• The Correlation Structure for a Class of Scene-Based Video Models and Its Impact on the Dimensioning of Video Buffers. M.M.Krunz and A.M.Ramasamy

• Hurst Parameter Estimation of Long-Range Dependent VBR MPEG Video Traffic in ATM Networks. S.H.Hong, R.Park and C.B.Lee

• Simple and Efficient Models for Variable Bit Rate MPEG Video Traffic. O.Rose

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Acronyms

MPEG Moving Pictures Expert Group

VBR Variable Bit Rate

CBR Constant Bit Rate

GOP Group of Pictures

ATM Asynchronous Transfer Mode

SRD Short Range Dependent

LRD Long Range Dependent

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MPEG-2 Video Theory

GOP = 12 IBBPBBPBBPBB

I Picture = Intra coded pictures

P Picture = Predictive coded pictures

B Picture = Bi-directionally coded pictures

2 min review on

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Field rate2 fields per frame

Frame rate29.97 frames per second (US) NTSC25 frames per second (Europe) PALLess for computers

Spatial encoding

Temporal encoding

MPEG-2 Video Theory

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CBR vs. VBR

CBR videoAdvantage:

• Fluid flow video model

• easier buffer management

• easier on the network

Disadvantage:

• Not bandwidth efficiente.g. If average video bandwidth is 1.5Mbps, but its spike are as high

as 3.5Mbps. Network must always guarantee 3.5Mbps

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CBR vs. VBR

VBR videoAdvantage:

• Bandwidth efficient

• Bursty

Disadvantage:

• Difficult to model

• Buffer management required

• Data rate control required

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Why model VBR video?

• Simulation• Analyze the stream for a particular network.

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How do you make sense out of this?

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Types of video modeling

• Probability density of Gamma/Pareto model ( modified bell shape)

• Scene-oriented model

• Markov chain model

• Histogram model (0th order Markov chain)

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Gamma/Pareto model

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Short Range Dependence (SRD)• Short time scale 10ms

• 200 frames

• Markov chain model, ARIMA process

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Long Range Dependent (LRD)

• Synonymous for “Hurst effect”

• Also know as “persistence phenomena”

Observation of an empirical record being significantly correlated to observation that are far removed in time

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Hurst value: 0.5 - ~0.75 Low activity~0.75 - ~0.9 Medium activity ~0.9 - 1 High activity

Hurst parameter is related to the amount of motion involved in the sequence

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Why simulate VBR video?

• Calculate minimum reservation rate. R*• Amount of buffering needed in the system

for it not to overflow

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Why simulate VBR video?

BANDWIDTHBitrate Bandwidth utilization

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Conclusion• LRD must be taken into consideration when

modeling VBR video• VBR video is content dependent• Bandwidth and buffer size depends on the

video mean bit rate• ATM systems:

Peak rate

Sustain rate

Average VBR rate

• Network characterization, for real-time VBR video.