Context-Based Adaptive Binary Arithmetic Coding in the H.264/AVC Video Compression Standard

Detlev Marpe, Heiko Schwarz, and Thomas Wiegand

IEEE Transactions on Circuits and Systems for Video Technology, JULY 2003

Outline

Introduction The CABAC Framework

Binarization Context Modeling Binary Arithmetic Coding

Example of detailed CABAC

Introduction (1)

CAVLC Baseline profile

CABAC Main profile

Compared to CAVLC, CABAC typically provides a reduction in bit rate between 5%~15%.

Introduction (2)

Binarization Context modeling Binary arithmetic coding

BinarizationBinarization Contextmodeling

Contextmodeling

Binaryarithmetic

coding

Binaryarithmetic

coding

entropy_coding_mode = 1

Binarization

Alphabet reduction Reduce a nonbinary syntax to a unique intermediat

e binary codeword. similar to converting a data symbol into a variable l

ength code but the binary code is further encoded prior to transmission.

Nothing is lost in terms of modeling. P(“3”) = P(C0)(“0”) P(C1)(“0”) P(C2)(“1”)

0

0

0 0 11

1

1C0

C1

C2 C3

“0” “3”“2” “1”

“5”-“30”

Binarization (2)

No multiplications needed Adaptive m-ary arithmetic coding requires

at least two multiplications for each symbol.

Enable context modeling on a subsymbol level. Conditional probabilities can be used for th

e most frequently observed bins, whereas others use zero-order probability model.

)()(

)1()()1()1()1()(

)1()1()1()(

nXnnnn

nXnnnn

xFlulu

xFlull

Binarization – four basic schemes (1)

Unary code (U) x = 4 11110

Truncated unary code (TU) x = 4, S = 5 11110 x = 5, S = 5 111110

kth order Exp-Golomb code (EGk)while (1) { if ( x >= (1<<k) ) { put ( 1 ) x = x – (1<<k) k++ } else { put ( 0 ) while ( k-- ) put ( (x>>k) & 0x01 ) break }}

prefix part

termination of prefix part

suffix part

Binarization – four basic schemes (2)

Fixed-length code (FL) S = 7 log27 = 3 Is applied to uniform distribution

Binarization – concatenation of basic schemes (1)

Coded_block_pattern Which blocks contain nonzero transform c

oefficients in a MB Prefix: 4-bit FL for luminance Suffix: TU with S = 2 for chrominance

Motion vector difference Prefix: TU with S = 9 for |mvd| < 9 Suffix: EG3 for |mvd - 9| if |mvd| 9 Sign bit

Binarization – concatenation of basic schemes (2)

Transform coefficient level Prefix: TU with S = 14 for |mvd| Suffix: EG0 for |mvd - 14| if |mvd| 14

Context Modeling (1)

A "context model" is a probability model for one or more bins of the binarized symbol.

This model may be chosen from a selection of available models depending on the statistics of recently-coded data symbols.

The context model stores the probability of each bin being "1" or "0".

Context Modeling (2)

Four basic design types Two neighboring syntax elements in the past of the

current syntax element

The prior coded bins (b0, b1, … bi-1) mb_type and sub_mb_type

The position in the scanning path Significant map

The accumulated number of encoded levels Coefficient levels

Residual data only

BCA

0

0

0 0 11

1

1C0

C1

C2 C3

“0” “3”“2” “1”

“5”-“30” mb_type (P/SP slices)

Context Modeling - Context index γ(1)

The entity of probability models can be arranged in a linear fashion such that each model can be identified by a so-called context index γ.

According to each context index γ, the probability model is determined by (αγ ,βγ) for 0≤ γ ≤398.

6 bits for αγ and 1 bit for βγ. αγ is the probability state ind

ex and the (binary) βγ represents the most probable symbol (MPS).

64 representative probability values

Context Modeling - Context index γ(2)

0 to 72 are related to syntax elements of macroblock, sub-macroblock, prediction modes of special and temporal as well as slice-based and macroblock-based control information. γ=ΓS+χS..

ΓS denotes the context index offset, the lower value of the range.

χS denotes the context index increment of a given syntax element S.

73 to 398 are related to the coding of residual data. Significant_coeff_flag and last_significant_coeff_flag are

conditioned on the scanning position. Coded_block_pattern: γ=ΓS+χS.. Others:γ=ΓS+ΔS(ctx_cat)+χS. Here the context category (ctx

_cat) dependent offset ΔS is employed.

Context Modeling - Context index γ(3)

Values of ΔS depending on context category and syntax element

Binary arithmetic coding

An arithmetic coder encodes each bin according to the selected probability model.

Binary arithmetic is based on the principal of recursive interval subdivision.

Another distinct feature in H.264/AVC is its simplicity bypass coding mode (assumed to be uniformly distributed).

1/4

3/4

2/3

1/3

5/6

1/61/2

1/2 1/2 1/2

1/2 1/2

Example of detailed CABAC – motion vector difference (1)

Binarization Prefix: TU (|mvdx|< 9) Suffix: EG3 (|mvdx| 9) |mvdx| = 10 prefix 8 use TU and suffix 2 use E

G3 Context model

One of 3 model is selected for bin 1, based on previous coded MVD values.

e=|mvdA|+|mvdB|

Example of detailed CABAC – motion vector difference (2)

The remaining bins are coded using one of 4 further context models:

e Context model for bin 1 (χS)

0 <= e < 3 Model 0

3 <= e < 32 Model 1

32 <= e Model 2

Bin Context mode

1 0, 1, or 2 (depend on ek)

2 3

3 4

4 5

5 6

6 and higher 7

Example of detailed CABAC – mb_type and sub_mb_type (2)

Binarization

Context model C0…C3 C’0…C’2

Experimental result

In our experiments, we compare the coding efficiency of CABAC to the coding efficiency of the baseline entropy coding method of H.264/AVC. The baseline entropy coding method uses the zero-order Exp-Golomb code for all syntax elements with the exception of the residual data, which are coded using the coding method of CAVLC.

Bit-rate savings of 9% to 14% are achieved, where higher gains are obtained at lower rates.