An Improved Context Adaptive Binary Arithmetic Coder …sim1mil/presentations/STreaming06_milani.pdf · STreaming Day 2006 An Improved Context Adaptive Binary Arithmetic Coder for

STreaming Day 2006

An Improved Context Adaptive Binary Arithmetic

Coder for the H.264/AVC standard

Simone Milani

Dept. of Information Engineering -University of Padova – Italy

E-mail: [email protected]

STreaming DayPisa, Sept. 11, 2006

2

Outline

Introduction to arithmetic coding

The H.264/AVC standard

Arithmetic coding of coefficients in the H.264/AVC standard

Modeling the probabilities through a Directed Acyclic Graph (DAG)

A Belief-Propagation based arithmetic coder

Experimental results


3

Video coding

Video compression algorithms allow the transmission or the storage of video sequences. The winning scheme is the one that is able to keep the distortion of the reconstructed sequence as low as possible despite the number of coded bits is significantly reduced

The most recent compression standards resort to adaptive arithmetic coding as an efficient solution to reduce the size of the coded bit stream

During the last decades, different video coding algorithms have been designed in order to achieve higher and higher compression performancefor different applications.

Video coding

Video Storage

Web TV

Video surveillance

Video Communication


4

The hybrid video coder H.264/AVC adopts two different entropy coding algorithms

The H.264/AVC video coding standard

De-blocking Filter

Motion Estimation

CAVLC

Scaling & Inv. Transform

Transform/ Scal./Quant.

Coder Control

Intra-frame Prediction

Motion-Compensation

Quant. Transf. coeffs

Motion data

Control data

+

++

-

Output video data

Inputslice(split into 16x16 macroblocks)

Output bitstream

CABAC

Context Adaptive Binary Arithmetic Coding

Context Adaptive Variable Length Coding


5

Arithmetic coding

00

0a

0c

1

111a

1c0

71

0

112a

2c

744⋅

741⋅

00

3a

3c

74832⋅⋅

114a

4c748

11⋅⋅

748109

2 ⋅⋅

74888

2 ⋅⋅

In the arithmetic coding, each binary string is mapped into an intervalaccording to the probability of the symbols that were coded.

Ex. [01101]

with

P(0)=[1/7,1/4,3/8,1/8,1/4]

respectively

In the adaptive approach, the probabilities of the binary symbols (i.e. the lengths of the intervals) are updated according to the statistics of the input signal. Each binary symbol is associated with a context that identifies a distinct probability distribution.

ContextIdentification

ContextProbability

Arithmetic Coder

Binary symbol

update


6

Context-Adaptive Binary Arithmetic CodingThe binarizer maps each syntax element into a variable-length binary string

The binarization allows a more efficient design of contexts

The binarizer also performs a partial “entropy coding” when the contexts have just been initialized and their probability distribution do not match the statistics of the input data yet.

The context modeler associates each symbol with a context that identifies a binary probability mass distribution


7

CABAC for H.264/AVC DCT coefficients(1/2)

Zero coefficients and signs

Coefficients absolute values

Each block is converted into a sequence of quantized transform coefficients using a zig-zag scan. Then zero coefficients and the signs are coded separately from the coefficients absolute values.

0

1 1

0

1

10As for zeros and signs, couples or triplets of binary values are sent to the arithmetic coder.

( ))(,, optionalsln n: 1 if coeff. != 0

l: 1 if the following coeff. are 0

s: 1 if coeff. > 0

The context depends on the scanning position.


8

CABAC for H.264/AVC DCT coefficients (2/2)

The absolute value of each non-zero coefficient is then binarized into a variable length string and each bit is sent to the binary arithmetic coder.

Binarizer

A context is associated to each bit according to its position in the string

ctx6ctx5ctx4ctx3ctx2ctx1ctx0

ctx

Each context identifies a distinct binary p. m. f.

)0(p )1(p

⇒

Note that contexts do not depend on the position

Arith. Coder

In our approach we skipped the binarization block


9

The correlation among coefficients

After the transform operation, the coefficients need to be rescaled since the transform is not orthonormal. As a consequence of the approximation of the rescaling factors, the basis function are not perfectly orthogonal.

Moreover the adopted transform dimension has a lower decorrelating property with respect to the previous coding standard.

Therefore, the coefficients of the transform result correlated.

At the same time, the coefficients of adjacent blocks are correlated

It is possible to take advantage of this

correlation to improve the coding algorithm, i.e. the

probability estimation mechanism


10

Modeling the bit probability through a DAG

•The correlation among different coefficient is well-modeled by a Directed Acyclic Graph (DAG)

•Each variable is associated with a coefficient in a 4 X 4 structure

•Each 4x4 structure can be associated either with the coefficients in a single transform block or with the coefficients of different blocks at equal frequencies in a macroblock.

In order to simplify the model, we adopted separate graphical models for different bit planes. This allowed the coder to deal with binary variables

Ising model


11

The adopted model

The performance of the arithmetic encoder was improved by modeling the least significant bit-planes in a block with DAGs.

As for the most significant bit-planes, the use of DAGs is not convenient since it increases the overall computational complexity. Since the bits different from zeros are limited and sparse for the upper levels, the traditional approach works well.


12

Ising Model

Through the Ising model it is possible to characterize the probability of a graph of binary variables.

Given πs the set of predecessors of s and θsz,xy=P(s=z/A=x,B=y) with

πs={A,B}, z,x,y={0,1} and s=a…p

)()/()/(),/(),/(),/(),/(),/()(

aPabPacPbafPgjkPhklPknoPlopPpaP

K

K =

BAssBAssBAssBAss

BAssBAssBAssyBAss

e

eap

spapbaP

pas

pass

⋅⋅+⋅−+⋅−⋅+−−

−⋅⋅+−−+−−⋅+−−−

⋅

=

=

∏

∏

=

=

11,1)1(11,0)1(01,1)1)(1(01,0

)1(10,1)1()1(10,0)1)(1(00,1)1)(1)(1(00,0)(

)/()()(

θθθθ

θθθθ

π

K

K

K


13

Coding process: probability estimate

The probability estimate is performed through a message passing algorithm

probability of vertical predecessor

probability of horizontal predecessor

Note that zero coefficients are known by coding run values.


14

Coding process: state transitions

In the old CABAC FSM the state transitiondepends on whether the coded bit corresponds or not to the most probable one (using a memory table).

In the new CABAC FSM, the state transition depends on the estimated probability. The new state is computed according to the equation

⎥⎦

⎥⎢⎣

⎢+⎟

⎠⎞

⎜⎝⎛= 5.0

0855.0)(log_ ixpstatenew


15

MAP estimate of the probabilities

The adopted model requires the estimate of the conditioned probabilities (or the moments of the Ising models)

The moments are computed using a log-MAP estimate

The estimate can be done either off-line on a set of training sequences or on-line using an adaptive algorithm.

Off-line computation On-line computation

∑

∑

∑

∑

=

=

=

=

−=

−−=

−−=

−−−=

M

iiii

s

M

iiii

s

M

iiii

s

M

iiii

s

BAsM

BAsM

BAsM

BAsM

111,0

110,0

101,0

100,0

)1(1ˆ

)1()1(1ˆ

)1)(1(1ˆ

)1)(1)(1(1ˆ

θ

θ

θ

θ

iiiss

iiiss

iiiss

iiiss

BAsBAsBAs

BAs

)1()1(ˆˆ)1()1()1(ˆˆ

)1)(1()1(ˆˆ)1)(1)(1()1(ˆˆ

11,011,0

10,010,0

01,001,0

00,000,0

−⋅−+⋅←−−⋅−+⋅←

−−⋅−+⋅←−−−⋅−+⋅←

αθαθαθαθαθαθαθαθ


16

Experimental results (1/2)

Results for the sequence “mobile” QCIF.

Parameter Value

GOP IPPP

GOP length 15

Frame rate 30 frame/s

RDOpt. Off


17

Experimental results (2/2)

Results for the sequence “mobile” CIF. Results for the sequence “salesman” QCIF.


18

Conclusions and future work

Two coding algorithms were designed. The first was based on the correlation among the coefficients of a block, the second was based on the correlation among the coefficients of a whole macroblock (but in different blocks).

The reduction in the coded bit stream size for the “mobile” sequence was about 12 %.

Future WorkEstimation of the probabilities from a fixed set of models (found through an EM

procedure)

Application of the algorithm with different binarizations and context structures

Implementation of the whole procedure in a fixed point arithmetic


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Bibliography

[1] J. Yedidia,W. Freeman, and Y.Weiss, “Constructing free energy approximations and generalized belief propagation algorithms,” IEEE Trans. Info. Theory, no. 7, pp. 2282–2312, July 2005.

[2] M. I. Jordan and Y. Weiss, “Graphical models: Probabilistic inference,” in The Handbook of Brain Theory and Neural Networks, M. A. Arbib, Ed. MIT Press, 2002.

[3] Joint Video Team (JVT) of ISO/IEC MPEG and ITU-T VCEG, “Joint Final Committee Draft (JFCD) of Joint Video Specification (ITU-T Rec. H.264 | ISO/IEC 14496-10 AVC)”, Joint Video Team 4th Meeting, Klagenfurt, Germany, Jul. 2002.

[4]Marpe, D.; Schwarz, H.; Wiegand, T. ,“Context-based adaptive binary arithmetic coding in the H.264/AVC video compression standard”, IEEE Trans. on CSVT, Vol. 13, Issue 7, Jul. 2003, pp. 620-636.

[5] Milani S.; Mian G. A.,“An Improved Context Adaptive Binary Arithmetic Coder for the H.264/AVC standard”, Proc. of the EUSIPCO 2006, Florence, Italy, Sept. 4-8, 2006


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Thank you for your attention !

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