reinventing compression: the new paradigm of distributed video coding bernd girod information...

Reinventing Compression:Reinventing Compression:The New Paradigm ofThe New Paradigm of

Distributed Video CodingDistributed Video Coding

Reinventing Compression:Reinventing Compression:The New Paradigm ofThe New Paradigm of

Distributed Video CodingDistributed Video Coding

BerndBernd Girod Girod

Information Systems LaboratoryInformation Systems LaboratoryStanford UniversityStanford University

B. Girod: Reinventing Compression: The New Paradigm of Distributed Video Coding 2

OutlineOutline

Lossless and lossy compression with receiver side information

Shifting the complexity of video encoding to the decoder

Error-resilient video transmission Image authentication


OutlineOutline





Encoder Encoder Decoder Decoder XX

YY

Lossless Compression Lossless Compression with Side Informationwith Side Information

R ≥ H(X|Y)

Statistically dependent


Y

R ≥ ?


Side Information

Side Information



YY

Lossless Compression Lossless Compression with Side Informationwith Side Information

R ≥ H(X|Y)



Y

R ≥ H(X|Y)


[Slepian, Wolf, 1973]


Towards Practical Slepian-Wolf CodingTowards Practical Slepian-Wolf Coding

• Convolution coding for data compression [Blizard, 1969]• Convolutional source coding [Hellman, 1975]• Syndrome source coding [Ancheta, 1976]

• Coset codes [Pradhan and Ramchandran, 1999]• Trellis codes [Wang and Orchard, 2001]• Turbo codes

[García-Frías and Zhao, 2001] [Bajcsy and Mitran, 2001] [Aaron and Girod, 2002]

• LDPC codes [Liveris, Xiong, and Georghiades, 2002]• . . .

• . . .


0 0.1 0.2 0.3 0.4 0.5 0.6 0.70

0.1

0.2

0.3

0.4

0.5

0.6

0.7

H(X|Y)

Rat

e

Slepian-Wolf boundRate = H(X|Y)

Rate-adaptive turbo codes

Rate-Adaptive Slepian-Wolf CodingRate-Adaptive Slepian-Wolf Coding

X X Turbo Decoder

Turbo Encoder

Parity bitsEncoder Buffer

Request bits

Y

L = 8192 bitsTotal simulated bits = 226


Encoder Encoder Decoder Decoder X X

YY

Encoder Encoder Decoder Decoder X X

Y

Lossy Compression with Side InformationLossy Compression with Side Information

[Wyner, Ziv, 1976] For MSE distortion and Gaussian statistics, rate-distortion functions of the two systems are the same.

[Zamir, 1996] The rate loss R*(d) – RX|Y (d) is bounded.

RX|Y (d)

R*(d)


Practical Wyner-Ziv CodingPractical Wyner-Ziv Coding

Wyner-Ziv Decoder

QuantizerSlepian-

Wolf Encoder

Wyner-Ziv Encoder

Slepian-

WolfDecode

r

Minimum Distortion

Reconstruction

Y Y

X 'XQ Q


Non-Connected Quantization RegionsNon-Connected Quantization Regions

Example: Non-connected intervals for scalar quantization

Decoder: Minimum mean-squared error reconstruction with side information

x

1q 2q 3q

x

| |X Yf x y 2

ˆ

conditional centroid

ˆ ˆ | ,arg min x

x E X x y q


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Interframe Video CodingInterframe Video Coding

PredictiveInterframe Decoder

PredictiveInterframe Decoder

PredictiveInterframe Encoder

PredictiveInterframe Encoder

Side Information

Y YX X’


Wyner-ZivInterframe Decoder

Wyner-ZivInterframe Decoder

Wyner-ZivIntraframe Encoder

Wyner-ZivIntraframe Encoder

Y

[Witsenhausen, Wyner, 1980] [Puri, Ramchandran, Allerton 2002][Aaron, Zhang, Girod, Asilomar 2002]

Low Complexity EncoderLow Complexity Encoder

X’

Side Information

X


Pixel-Domain Wyner-Ziv Video CodecPixel-Domain Wyner-Ziv Video Codec

Interframe Decoder

Scalar Quantizer

Turbo Encoder

Buffer

WZ frames

X

Intraframe Encoder

Turbo Decoder

Request bits

Slepian-Wolf Codec

Interpolation/ Extrapolation

Reconstruction

Y

Key frames

I Conventional Intraframe coding

Conventional Intraframe decoding

X’

I’

Side information

[Aaron, Zhang, Girod, Asilomar 2002]


Decoder side informationgenerated by motion-

compensated interpolationPSNR 30.3 dB

After Wyner-Ziv Decoding16-level quantization – 1.375 bpp

11 pixels in errorPSNR 36.7 dB




Decoder side informationgenerated by motion-

compensated interpolationPSNR 24.8 dB

After Wyner-Ziv Decoding16-level quantization – 2.0 bpp

0 pixels in errorPSNR 36.5 dB


Yk

IDCT

DCT-Domain Wyner-Ziv Video CodecDCT-Domain Wyner-Ziv Video Codec

Request bits

Interpolation/ Extrapolation

Recon

I Conventional Intraframe coding

Conventional Intraframe decoding

DCT

For each transform band k

I’

W’

Y

Scalar Quantizer

DCTTurbo

EncoderBuffer

Turbo Decoder

Side information

WZ frames

W

Key frames

Xk Xk’

Interframe Decoder

Intraframe Encoder


Rate-Distortion Performance - SalesmanRate-Distortion Performance - Salesman

Every 8th frame is a key frame

Salesman QCIF sequence at 10fps 100 frames

6 dB

3 dB

B frame ~ 100%

WZ DCT ~ 7%

WZ Pixel ~ 6%

I frame ~ 18%

Interframe 100%

Encoder Runtime Pentium 1.73 GHz machine


Rate-Distortion Performance – Hall MonitorRate-Distortion Performance – Hall Monitor

8 dB

3 dB

Every 8th frame is a key frame

Hall Monitor QCIF sequence at 10fps 100 frames

DCT-based Intracoding 149 kbps

PSNRY=30.0 dB

Wyner-Ziv DCT codec 152 kbps

PSNRY=35.6 dB GOP=8

Salesman at 10 fps

DCT-based Intracoding 156 kbps

PSNRY=30.2 dB

Wyner-Ziv DCT codec 155 kbps

PSNRY=37.1 dB GOP=8

Hall Monitor at 10 fps


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Systematic Lossy Source/Channel CodingSystematic Lossy Source/Channel Coding

Information theoretic optimality conditions [Shamai, Verdú, Zamir, 1998] Enhancing analog image transmission using digital side information

[Pradhan, Ramchandran, 2001] Lossy source-channel coding of video waveforms

[Rane, Aaron, Girod, 2004,’05,’06]

Encoder Digital Channel

Decoder

Analog Channel

Wyner-ZivEncoder

Sideinfo

Wyner-ZivDecoder

Digital Channel

Wyner-ZivEncoder

Sideinfo

Wyner-ZivDecoder

Digital Channel


Systematic Lossy Error Protection (SLEP)Systematic Lossy Error Protection (SLEP)

VideoEncoder

Video Decoder With Error Concealment

InputVideo

VideoWith Errors

Cha

nnel

Wyner-ZivEncoder

Wyner-ZivDecoder

Side Information

OutputVideo

“Analog Channel”


SLEP using H.264/AVC Redundant SlicesSLEP using H.264/AVC Redundant Slices

Encode Redundant Pic(Requantize)

Entropy Decoding

WYNER-ZIV ENCODER WYNER-ZIV DECODER

Err

or-p

rone

Cha

nnel

Decode Redundant Slice

Motion Vecs +Coding Modes

ErasureDecoding

Side info

Motion Vecs +Coding Modes

QP

Recovered motion vectors for erroneously received primary slices

EncodePrimary Pic

Q-1 T-1

H.264/AVC DECODER

+Entropy Decoding

OutputVideo

H.264/AVC ENCODER

InputVideo

Encode Redundant Pic(Requantize)

RSEncoder

Parity SlicesQP

MC

Foreman @ 408 kbps, error resilience bit rate = 40 kbpsSymbol error probability = 5 x 10-4

QP = 28QP = 2835.7 dB35.7 dB

Error concealment onlyError concealment only40 kbps FEC40 kbps FECSLEP with redundant QP = 36SLEP with redundant QP = 36SLEP with redundant QP = 40SLEP with redundant QP = 40SLEP with redundant QP = 48SLEP with redundant QP = 4820.9 dB20.9 dB25.5 dB25.5 dB30.9 dB30.9 dB34.2 dB34.2 dB32.9 dB32.9 dB

Error-free After error propagation

100 kbps FEC

PSNR: 32.5 dB

Recovered 53.7 % of lost macroblocks

100 kbps Wyner-Ziv bit stream

PSNR: 38.0 dB

Recovered 96.6 % of lost macroblocks

Foreman @ 1 MbpsSymbol error probability = 2 x 10-4

Rally, 1 Mbps, 3% packet loss

80 kbps Wyner-Ziv bit stream38.1 dB

80 kbps FEC33.4 dB

Recovered 67.5 % of lost macroblocks Recovered 97.1 % of lost macroblocks


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Media Authentication ProblemMedia Authentication Problem

Legimate degradation(e.g., compression)

Illegimate degradation(e.g., compression + tampering)

How to distinguish legimate and illegimate signal degradationswithout access to the original?

How to distinguish legimate and illegimate signal degradationswithout access to the original?

Original

Received


Image Authentication by Distributed CodingImage Authentication by Distributed CodingOriginal Received

Coarse approximationor (random) projection

Slepian-Wolfcoder

Slepian-Wolfdecoder

?

Side information

[Lin, Varodayan, Girod, ICIP 2007][Lin, Varodayan, Girod, MMSP 2007]


Image Authentication by Distributed CodingImage Authentication by Distributed Coding

Two-state Channel

Original Imagex

Image-to-be-authenticatedy

Two-state Channel

Original Imagex


Slepian-WolfEncoder

Slepian-Wolf Decoder

Slepian-Wolf Bitstream S(X)

Image Projection

X

RandomProjection

Side Information YRandom

ProjectionReconstructed Image

Projection X’

Random Seed Ks

Quantization

Random Seed Ks

Two-state Channel

Original Imagex


Slepian-WolfEncoder

Slepian-Wolf Decoder

Cryptographic Hash Function

Cryptographic Hash Function

DigitalSignatureD(X,Ks)

ImageDigest

Comparison

Slepian-Wolf Bitstream S(X)

Image Projection

X

RandomProjection

Side Information YRandom

ProjectionReconstructed Image

Projection X’

Asymmetric Encryption

Asymmetric Decryption

ImageDigest

Private Key Public Key

Random Seed Ks

Quantization

Random Seed Ks

[Lin, Varodayan, Girod, ICIP 2007]


Minimum Rate for Successful DecodingMinimum Rate for Successful Decoding

28 30 32 34 36 38 40 420

0.001

0.002

0.003

0.004

0.005

0.006

0.007

0.008

0.009

0.01

Reconstruction PSNR (dB)

Rat

e (b

its p

er p

ixel

of o

rigin

al im

age)

Minimum Rate for Illegitimate JPEG2000 State

Minimum Rate for Illegitimate JPEG State

Minimum Rate for Legitimate JPEG2000 StateMinimum Rate for Legitimate JPEG State

Selected Slepian-Wolf Bit Rate

Experiment:JPEG or JPEG2000 compression+ illegimate text banner

[Lin, Varodayan, Girod, ICIP 2007]


DemoDemo


Distributed Image/Video Coding:Distributed Image/Video Coding:Why Do We Care?Why Do We Care?

New Paradigm: Chance to Reinvent Compression from Scratch– Entropy coding– Quantization– Signal transforms– Adaptive coding– Rate control– . . .

Powerful New Tool in the Compression Tool-Box– Very low complexity encoders– Compression for networks of cameras– Error-resilient transmission of signal waveforms– Digitally enhanced analog transmission– Unequal error protection without layered coding– Image authentication – Random access– Compression of encrypted signals– . . .

Further interest:

B. Girod, A. Aaron, S. Rane, D. Rebollo-Monedero, "Distributed Video Coding," B. Girod, A. Aaron, S. Rane, D. Rebollo-Monedero, "Distributed Video Coding," Proceedings of the IEEE,Proceedings of the IEEE, Special Issue on Video Coding and Delivery. Special Issue on Video Coding and Delivery. January 2005.January 2005.

http://www.stanford.edu/~bgirod/pdfs/DistributedVideoCoding-IEEEProc.pdf http://www.stanford.edu/~bgirod/pdfs/DistributedVideoCoding-IEEEProc.pdf

reinventing compression: the new paradigm of distributed video coding bernd girod information...

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