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

1. 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

2. Outline • Introduction • The CABAC Framework • Binarization • Context Modeling • Binary Arithmetic Coding • Example of detailed CABAC

3. Introduction (1) • CAVLC • Baseline profile • CABAC • Main profile • Compared to CAVLC, CABAC typically provides a reduction in bit rate between 5%~15%.

4. Introduction (2) • Binarization • Context modeling • Binary arithmetic coding entropy_coding_mode = 1 Binarization Context modeling Binary arithmetic coding

5. C0 0 1 C1 0 1 “5”-“30” C2 C3 1 0 1 0 “0” “3” “2” “1” Binarization • Alphabet reduction • Reduce a nonbinary syntax to a unique intermediate binary codeword. • similar to converting a data symbol into a variable length 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”)

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

7. 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

8. Binarization – four basic schemes (2) • Fixed-length code (FL) • S = 7  log27 = 3 • Is applied to uniform distribution

9. Binarization – concatenation of basic schemes (1) • Coded_block_pattern • Which blocks contain nonzero transform coefficients 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

10. Binarization – concatenation of basic schemes (2) • Transform coefficient level • Prefix: TU with S = 14 for |mvd| • Suffix: EG0 for |mvd - 14| if |mvd|  14

11. 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".

12. 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 B A C C0 0 1 C1 0 1 mb_type (P/SP slices) “5”-“30” C2 C3 Residual data only 1 0 1 0 “0” “3” “2” “1”

13. 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. • αγis the probability state index and the (binary) βγrepresents the most probable symbol (MPS). 64 representative probability values

14. 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 ΔSis employed.

15. Context Modeling - Context index γ(3) • Values ofΔSdepending on context category and syntax element

16. 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). 1/6 1/4 2/3 1/2 1/2 1/2 5/6 3/4 1/2 1/2 1/2 1/3

17. 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 EG3 • Context model • One of 3 model is selected for bin 1, based on previous coded MVD values. • e=|mvdA|+|mvdB|

18. Example of detailed CABAC – motion vector difference (2) • The remaining bins are coded using one of 4 further context models:

19. Example of detailed CABAC – mb_type and sub_mb_type (2) • Binarization • Context model • C0…C3 • C’0…C’2

20. 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.