Context-based adaptive binary arithmetic coding in the H.264/AVC video compression

1
Context-based adaptive binary arithmetic coding
in the H.264/AVC video compression
CABAC

• IEEE CSVT July 2003
• Detlev Marpe, Heiko Schwarz, and Thomas Wiegand
• 2003/11/04
• Presented by Chen-hsiu Huang

2
Outline

• Introduction
• The CABAC framework
• Detailed description of CABAC
• Experimental result
• Conclusion

3
Past deficiencies

• Entropy coding such as MPEG-2, H.263, MPEG-4 (SP)
  is based on fixed tables of VLCs.
• Due to VLCs, coding events with probability gt 0.5
  cannot be efficiently represented.
• The usage of fixed VLC tables does not allow an
  adaptation to the actual symbol statistics.
• Since there is a fixed assignment of VLC tables
  and syntax elements, existing inter-symbol
  redundancies cannot be exploited.

Why?
4
Solutions
Jump!

• The first hybrid block-based video coding schemes
  that incorporate an adaptive binary arithmetic
  coder was presented in 6.
• The first standard that use arithmetic entropy
  coder is given by Annex E of H.263 4.
• However, the major drawbacks contains
• Annex E is applied to the same syntax elements as
  the VLC elements of H.263.
• All the probability models an non-adaptive that
  their underlying probability as assumed to be
  static.
• The generic m-ary arithmetic coder used involves
  a considerable amount of computational complexity.

5
The CABAC Framework

• binarization ? context modeling ? binary
  arithmetic coding

Figure 1.
6
Binarization
Back

• Consider the value 3 of mb_type, which signals
  the macroblock type P_8x8, is given by 001.
• The symbol probability p(3) is equal to the
  product of p(C0)(0), p(C1)(0), and
  p(C2)(1), where C0, C1, and C2 are denote the
  binary probability models of the internal nodes.

Figure 2.
7

• Adaptive m-ary binary arithmetic coding (m gt 2)
  is in general requiring at least two
  multiplication for each symbol to encode as well
  as a number of fairly operations to perform the
  probability update 36.
• Contrary, fast, multiplication-free variants of
  binary arithmetic coding, one of which was
  specifically developed for the CABAC frame, as
  described below.
• Since the probability of symbols with larger bin
  strings is typically very low, the computation
  overhead in fairly small and can be easily
  compensated by using a fast binary coding engine.
• Finally, binarization enables context modeling on
  sub-symbol level. For the most frequently
  observed bins, conditional probability can be
  used, while less frequently observed bins can be
  treaded using a joint, typically zero-order
  probability model.

Why?
8
Binarization Schemes

• A binary representation for a given non-binary
  valued syntax element should be close to a
  minimum redundancy code.
• Instead of Huffman tree, the design of CABAC
  (mostly) relies the a few basic code trees, whose
  structure enables a simple on-line computation of
  all code words without the need for storing any
  tables.
• Unary code (U) and truncated unary code (TU)
• The kth order Exp-Golomb code (EGk)
• The fixed-length code (FL)
• All the binarization schemes have less
  probability when the codeword length becomes
  longer.
• In addition, there are binarization schemes based
  on a concatenation of these elementary types.
• As an exception, there are five specific binary
  trees selected manually for the coding of
  macroblock and sub-macroblock types. Two of them
  show in Figure 2.

9
Unary and Truncated Unary Binarization

• For each unsigned integer valued symbol x gt 0,
  the unary code word in CABAC consists if x 1
  bits plus a terminating 0 bit.
• The truncated unary (TU) code is only defined for
  x with 0 lt x lt S, where for x lt S the code is
  given by the unary code, whereas for xS the
  terminating 0 bit is neglected.
• For example
• U 5 gt 111110
• TU with S9
• 6 gt 1111110
• 9 gt 111111111

10
kth order Exp-Golomb Binarization

• The prefix part of the EGk codeword consists of a
  unary code corresponding to the value
  l(x)floor(log2(x/2k1))
• The EGk suffix part is computed as the binary
  representation of x2k(1-2l(x)) using kl(x)
  significant bits.

11
Fixed-Length Binarization

• Let x denote a given value of such a syntax
  element, where 0 lt x lt S. Then, the FL codeword
  of x is simply given by the binarization
  representation of x with a fixed (minimum) number
  lFLceil(log2S) of bits.
• Typically, FL binarization is applied to syntax
  elements with a nearly uniform distribution or to
  syntax elements, where each bit in the FL binary
  representation represents a specific coding
  decisions.
• E.g. In the part of the coded block pattern
  symbol related to the luminance residual data.

12
Concatenation schemes

• Three binarization schemes are derived
• Concatenation of a 4-bit FL prefix as a
  representation of the luminance related part of
  the coded block pattern and a TU suffix with S2
  representing the chrominance related part of
  code_block_pattern.
• Both the second and third are derived from the TU
  and the EGk binarization, which are referred as
  Unary/kth order Exp-Golomb (UEGk) binarization,
  are applied to motion vector differences and
  absolute values of transform coefficients levels.

13

• The design of these concatenated binarization
  scheme is motivated by the following
  observations
• First, the unary code is the simplest prefix-free
  code in terms of implementation cost.
• Second, it permits a fast adaptation of the
  individual symbol probabilities in the
  sub-sequent context modeling stage, since the
  arrangement of the nodes in the corresponding
  tree is typically such that with increasing
  distance of the internal nodes from the root node
  the corresponding binary probabilities are less
  skewed.
• These observations are only accurate for small
  values of the absolute motion vector differences
  and transform coefficient levels. For larger
  values, there is not much use of an adaptive
  modeling leading to the idea of concatenating and
  adaptation.

14
E.g. mvd, motion vector difference

• For the prefix part of the UEGk bin string, a TU
  binarization with a cutoff S9 is involed for
  min(mvd, 9).
• If mvd is equal to zero, the bin string consists
  only the prefix codeword 0.
• If the condition mvd gt 9 holds, the suffix in
  constructed as an EG3 codeword for the value of
  mvd - 9, to which the sign of mvd is appended
  using the sign bit 1 for a negative mvd and 0
  otherwise. For mvd values with 0 lt mvd lt 9, the
  suffix consists only of the sign bit.

15

• With the choice of the Exp-Golomb parameter k3,
  the suffix code words are given such that a
  geometrical increase of the prediction error in
  units of two samples is captured by a linear
  increase in the corresponding suffix code word
  length.

Figure 3. UEG0 binarization for encoding of
absolution values of transform coefficient levels.
16
Context modeling

• Suppose a pre-defined set T of past symbols, a
  so-called context template, and a related set
  C0,,C-1 of contexts is given, where the
  context are specified by a modeling function
  FT?C operating on the template T.
• For each symbol x to be code, a conditional
  probability p(xF(z)) is estimated by switching
  between different probability models according to
  the already coded neighboring symbols z in T.
  Thus, p(xF(z)) is estimated on the fly by
  tracking the actual source statistics.
• The number t of different conditional
  probabilities to be estimated for an alphabet
  size of m is equal to tC(m-1).
• This implies that by increasing the number of C,
  there is a point where overfitting of the model
  may occur.

17

• In CABAC, only very limited context templates T
  consisting of a few neighboring of the current
  symbol to encode are employed such that only a
  small number of different context models C is
  used.
• Second, context modeling is restricted to select
  bins of the binarized symbols. As a result, the
  model cost is drastically reduced.
• Four basic design types of context models can be
  distinguished in CABAC. The first type involves a
  context template with up to two neighboring
  syntax elements in the past of the current syntax
  element to encode.

Figure 4. illustration of a context template
consisting of two neighboring syntax element A
and B to the left and on top of the current
syntax element C.
18
Types of context modeling

• The second type of current is only defined for
  the syntax elements of mb_type and sub_mb_type.
• For this kind of context models, the values of
  prior coded bins (b0,b1,...,bi-1) are used for
  the choice of a model for a given bin with index
  i.

Note that in CABAC these context models are only
used to select different models for different
internal nodes of the corresponding binary trees.
Figure 2.
19

• Both the third and fourth type of context models
  is applied to residual data only. In contrast to
  all other types of context models, both types
  depend on the context categories of different
  block types.
• The third type does not rely on past coded data,
  but on the position in the scanning path.
• Significant map
• The fourth type, modeling functions are specified
  that involve the evaluation of the accumulated
  number of encoded/decoded levels with a specific
  value prior to the current level bin to
  encode/decode.
• Level information

20
Context index ?

• The entity of probability models used in CABAC
  can be arranged in a linear fashion called
  context index ?.
• Each probability model relate to a given context
  index ? is determined by two values, a 6-bit
  probability state index a? and the (binary) ß? of
  the most probable symbol (MPS).
• (a? ß?,) for 0 ? 398 represented as 7-bit
  unsigned integer.

Figure 5. syntax elements and associated range of
context indices
21

• The context indices in the range from 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.
• For this type, a corresponding context index ?
  can be calculated as ?GS?S.. GS denotes the
  context index offset, which is defined as the
  lower value of the range given in Figure 5. And
  ?S denotes the context index increment of a given
  syntax element S.
• Context indices of from 73 to 398 are related to
  the coding of residual data.
• The range value in the lower row of the
  corresponding syntax elements in Figure 5 specify
  the context indices for field-based coding mode.
  In pure frame only 277 out of the total 399
  probabilities models are actually used.

22
Back

• For other syntax elements of residual data, a
  context index ? is given by ?GS?S(ctx_cat)?S.
  Here the context category (ctx_cat) dependent
  offset ?S is employed. (Figure 6)
• Note that only past coded value of syntax
  elements are evaluated that belong to the same
  slice, where the current coding process takes
  place.

Figure 6. Basic types with number of coefficients
and associated context categories.
23
Binary arithmetic coding

• Binary arithmetic is based on the principal of
  recursive interval subdivision.
• Suppose that an estimate of the probability pLPS
  in (0,0.5 of the least probable symbol (LPS) is
  given and its lower bound L and its width R.
  Based on this, the given interval is sub-divided
  into two sub-intervals RLPSRpLPS (3), and the
  dual interval is RMPSR-RLPS.
• In a practical implementation, the main
  bottleneck in terms of throughput is the
  multiplication operation required.
• A significant amount of work has been published
  aimed at speeding up the required calculation by
  introducing some approximations of either the
  range R or of the probability pLPS such that
  multiplication can be avoided. 32-34

24

• The Q coder 32 and QM and MQ coder 35 both
  have their inefficiency. Here we designed an
  alternative multiplication-free one, called
  modulo coder (M coder), shown to provide a higher
  throughout than the MQ coder 36.
• The basic idea of M coder is to project both the
  legal range Rmin,Rmax) of interval width R and
  the probability range with the LPS onto a small
  set of representative QQ0,...,QK-1,
  Pp0,...,pN-1. Thus the multiplication on the
  right-hand side of (3) can be approximated by
  using a table of KN pre-computed values.
• A reasonable size of the corresponding table and
  a sufficient good approximation was found by
  using a set Q of K4 quantized range values
  together with a set P of M64 LPS related
  probability values.
• Another distinct feature in H.264/AVC, as already
  mentioned above, is its simplicity bypass coding
  mode (assumed to be uniformly distributed).

25
Details of CABAC

• The syntax elements are divided into two
  categories.
• The first contains elements related to macroblock
  type, sub-macroblock type, and information of
  prediction modes both of spatial and of temporal
  type as well as slice and macroblock-based
  control information.
• In the second, all residual data elements, i.e.,
  all syntax elements related to the coding of
  transform coefficients are combined.
• In addition, a more detailed explanation of the
  probability estimation process and the
  table-based binary arithmetic coding engine of
  CABAC is given.

26
Coding of macroblock type, prediction mode, and
control information

• At the top level of the macroblock layer syntax
  the signaling of mb_skip_flag and mb_type is
  performed. The binary-valued mb_skip_flag
  indicates whether the current macroblock in a
  P/SP or B slice is skipped.
• For a given macroblock C, the related context
  models involves the mb_skip_flag values of the
  neighboring A at left and B on top. Given by
• ?MbSkip(C) (mb_skip_flag(A) ! 0) ? 0 1
  (mb_skip_flag(B) ! 0) ? 0 1
• If one or both of the neighboring A or B are not
  available, the mb_skip_type (C) value is set to 0.

27
Macroblock type

• As already stated above. Figure 2 shows the
  binarization trees for mb_type and sub_mb_type
  that are used in P/SP slices.
• Note the mb_type value of 4 for P slices is not
  used in CABAC entropy coding mode. For the values
  5-30 of mb_type, which is further specified
  in 1.
• For coding a bin value corresponding to the
  binary decision at an internal node shown in
  Figure 2, separate context models denote by
  C0,...,C3 for mb_type and C0,...,C3 for
  sub_mb_type are employed.

?Figure 2
28
Coding of prediction modes

• Intra prediction modes for luma 4x4 the
  luminance intra prediction modes for 4x4 blocks
  are itself predicted resulting in the syntax
  elements of the binary-values prev_intra4x4_pred_m
  ode_flag and rem_intra4x4_pred_mode, where the
  latter is only present if the former takes a
  value of 0.
• For coding these syntax elements, two separate
  probability models are utilized one for coding
  of the flag and another for coding each bin value
  of the 3-FL binarization value of
  rem_intra4x4_pred_mode.

29

• Intra prediction modes for chroma
• ?ChPerd(C) (ChPredInDcMode(A) ! 0) ? 0 1
  (ChPredInDcMode(B) ! 0) ? 0 1
• Reference Picture Index
• ?RefIdx(C) (RefIdxZeroFlag(A) ! 0) ? 0 1 2
  ((RefIdxZeroFlag(B) ! 0) ? 0 1)
• Components of motion vector differences
• mvd(X,cmp) denote the value of a motion vector
  difference component of direction cmp in hori,
  vert related to a macroblock or sub-macroblock
  partition X.

30

• Macroblock-based quantization parameter change
• For updating the quantization parameter on a
  macroblock level, mb_qp_delta is present for each
  non-skipped macroblock. For coding the signed
  value d(C) of this syntax element, d(C) is first
  mapped onto a positive value by
• d(C)2 d (C)-((d(C)gt0) ? 1 0)
• Then d(C) is binarized using the unary
  binarization scheme.
• End of slice flag
• For signaling the last macroblock (macroblock
  pair) in a slice, the end_of_slice_flag is
  present for each macroblock (pair).
• The event of non-terminating macroblock is
  related to the highest possible MPS possibility
• Macroblock pair field flag
• ?MbField(C) mb_field_decoding_flag(A)
  mb_field_decoding_flag(B)

31
Coding of residual data

• A one-bit symbol coded_block_flag and a
  binary-valued significant map are used to
  indicate the occurrence and the location of
  non-zero transform coefficients in a given block.
• Non-zero levels are encoded in reverse scanning
  order.
• Context models for coding of nonzero transform
  coefficients are chosen based on the number of
  previously transmitted nonzero levels within the
  reverse scanning path.

32

• First the coded block flag is transmitted for the
  given block of transform coefficients unless the
  coded block pattern or the macroblock mode
  indicated that the regarded block has no nonzero
  coefficients.
• If the coded block flag is nonzero, a significant
  map specifying the position of significant
  coefficients is encoded.
• Finally, the absolute value of the level as well
  as the sign is encoded for each significant
  transform coefficient.

Figure 7.
33
Encoding process of residual data

• Coded block pattern For each non-skipped
  macroblock with prediction mode not equal to
  intra_16x16, the coded_block_pattern symbol
  indicates which of the six 8x8 blocks four
  luminance and two chrominance contain nonzero
  transform coefficients.
• A given value of the syntax element
  coded_block_pattern is binarized using the
  concatenation of a 4-bit FL and a TU binarization
  with cutoff value S2.
• Coded block flag is a one-bit symbol, which
  indicate if there are significant, i.e. nonzero
  coefficients inside single block of transform
  coefficients.
• Scanning of transform coefficients the 2-D array
  of transform coefficient levels of those
  sub-blocks for which the coded_block_flag
  indicates nonzero entries are first mapped onto a
  1D list using a given scanning pattern.

34

• Significance map If the significant_coeff_flag
  symbol is one, a further one-bit symbol
  last_significant_coefficient is sent. This symbol
  indicates if the current significant coefficient
  is the last in inside the block or if further
  significant coefficients follow.
• Level information The value of significant
  coefficients (levels) are encoded by using two
  coding symbols coeff_abs_level_minus1, and
  coeff_sign_flag. The UEG0 binarization scheme is
  used for encoding of coeff_abs_level_minus1.
• The levels are transmitted in reverse scanning
  order allowing the usage of reasonable adjust
  context models.

35
Context models for residual data

• Context modes for residual data In H.264/AVC,
  there 12 types of transform coefficient blocks,
  which typically have different kinds of
  statistics. To keep the number of different
  context models small, they are classified into
  five categories as in Figure 6.
• For each of these categories, a special set of
  context models is used for all syntax elements
  related to residual data.
• coded_block_pattern For bin indices from 0 to 3
  corresponding to the four 8x8 luminance blocks,
• ?CBP(C,bin_idx) ((CBP_Bit(A) ! 0) ? 0 1)
  2((CBP_Bit(B) ! 0) ? 0 1)
• For indices 4 and 5, are specified in 1

?Figure 6
36

• Coded Block Flag Coding of the coded_block_flag
  utilizes four different probability models for
  each of the five categories as specified in
  Figure 6.
• ?CBFlag(C) coded_block_flag(A)
  2coded_block_flag(B)
• Significant map For encoding the significant
  map, up to 15 different probability models are
  used for both significant_coeff_flag and
  last_significant_flag.
• The choice of the models and the context index
  increments depend on the scanning position
• ?SIG(coeffi) ?LAST(coeffi) i
• Level information Reverse scanning of the level
  information allows a more reliable estimation of
  the statistics, because at the end of the
  scanning path it is very likely to observe the
  occurrence of successive so-called trailing 1s.

37
Probability estimation

• For CABAC, 64 representative probability values
  ps in 0.01875, 0.5 were derived for the LPS by
• Psa Ps-1 for all s1,...,63
• a(0.01875 / 0.5)(1/63) and p00.5

Figure 8. LPS probability values and transition
rules for updating the probability estimation of
each state after observing a LPS (dashed lines in
left direction) and a MPS (solid lines in right
direction).
38

• Both the chosen scaling factor a 0.95 and the
  cardinality N64 of the set probabilities
  represent a good compromise between the desire
  for fast adaptation (a ? 0, small N) and
  sufficiently stable and accurate estimate (a ?
  1, large N).
• As a result of this design, each context model in
  CABAC can be completely determined by two
  parameters its current estimate of the LPS
  probability and its value of MPS ß being either 0
  or 1.
• Actually, for a given probability state, the
  update depends on the state index and the value
  of the encoded symbol identified either as a LPS
  or a MPS.
• The derivation of the transition rules for the
  LPS probability is based on the following
  relation between a given LPS probability pold and
  its updated counterpart pnew

39
Table-based binary arithmetic coding

• Actually, the CABAC coding engine consists of two
  sub-engines, one for regular coding mode and the
  other for bypass coding engine.
• Interval sub-division in regular coding mode The
  internal state of the arithmetic encoding engine
  is as usual characterized by two quantities the
  current interval R and the base L of the current
  code interval.

Figure 9.
40

• First, the current interval R is approximated by
  a quantized value Q(R), using an equi-partition
  of the whole range 28Rlt29 into four cells. But
  instead of using the corresponding representative
  quantized values Q0, Q1, Q2, and Q3. Q(R) is only
  addressed by its quantizer index ?, e.g. ?(Rgtgt6)
  3.
• Thus, this index and the probability state index
  are used as entries in a 2D table TabRangeLPS to
  determine (approximate) the LPS related
  sin-interval range RLPS. Here the table
  TabRangeLPS contains all 64x4 pre-computed
  product values psQ? for 0s63, and 0 ?3 in 8
  bit precision.

41

• Bypass coding mode To speed up the
  encoding/decoding of symbols, for which R-RLPS
  RLPS R/2 is assumed to hold.
• The variable L is doubled before choosing the
  lower or upper sub-interval depending on the
  value of the symbol to encode (0 or 1).
• In this way, doubling of L and R in the
  sub-sequent renormalization in the bypass is
  operated with doubled decision threshold.

Figure 10.
42

• Renormalization and carry-over control A
  renormalization operation after interval
  sub-division is required whenever the new
  interval range R no longer stays with its legal
  range of 28,29).
• For the CABAC engine, the renormalization process
  and carry-over control of 37 was adopted.
• This implies, in particular, that the encoder has
  to resolve any carry propagation by monitoring
  the bits that are outstanding for being emitted.
• More details can be found in 1.

43
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
  1, 2.
• For the range of acceptable video quality for
  broadcast application of about 3038 dB and
  averaged over all tested sequences, bit-rate
  savings of 9 to 14 are achieved, where higher
  gains are obtained at lower rates.

44
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45
References
Back

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  Feb. 1989.
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• 36 D. Marpe and T.Wiegand, A highly efficient
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  Barcelona, Spain, Sept. 2003.

46
Q1.
Back

• The problem with this scheme lies in the fact
  that Huffman codes have to be an integral number
  of bits long.
• The optimal number of bits to be used for each
  symbol is the -log2(1/p), where p is the
  probability of a given character.
• Thus, if the probability of a character is 1/256,
  such as would be found in a random byte stream,
  the optimal number of bits per character is log
  base 2 of 256, or 8.
• If the probability goes up to 1/2, the optimum
  number of bits needed to code the character would
  go down to 1.
• If a statistical method can be developed that can
  assign a 90 (gt 0.5) probability to a given
  character, the optimal code size would be 0.15
  bits. The Huffman coding system would probably
  assign a 1 bit code to the symbol, which is 6
  times longer than is necessary.

47
Q2.
Back

• For each symbol to encode, the upper bound u(u)
  and low bound l(l) of the interval containing the
  tag for the sequence must be computed.

48
H.264 / MPEG-4 Part 10 Introduction to CABAC

• When entropy_coding_mode is set to 1, an
  arithmetic coding system is used to encode and
  decode H.264 syntax elements.
• The arithmetic coding scheme selected for H.264,
  Context-based Adaptive Binary Arithmetic Coding
  or CABAC, achieves good compression performance
  through
• Selecting probability models for each syntax
  element according to the elements context,
• Adapting probability estimates based on local
  statistics and
• Using arithmetic coding.

49
Coding stages

• Binarization
• CABAC uses Binary Arithmetic Coding which means
  that only binary decisions (1 or 0) are encoded.
• A non-binary-valued symbol (e.g. a transform
  coefficient or motion vector) is "binarized" or
  converted into a binary code prior to arithmetic
  coding.
• This process is similar to the process of
  converting a data symbol into a variable length
  code but the binary code is further encoded (by
  the arithmetic coder) prior to transmission.

50

• Context model selection
• 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".
• Arithmetic encoding
• An arithmetic coder encodes each bin according to
  the selected probability model.
• Note that there are just two sub-ranges for each
  bin (corresponding to "0" and "1").
• Probability update
• The selected context model is updated based on
  the actual coded value (e.g. if the bin value was
  "1", the frequency count of "1"s is increased).
• Above stages are repeated for each bit (or bin)
  of the binarized symbol.

51
The coding process

• Binarization. We will illustrate the coding
  process for one example, MVDx (motion vector
  difference in the x-direction).
• Binarization is carried out according to the
  following table for MVDxlt9 (larger values of
  MVDx are binarized using an Exp-Golomb codeword).
• (Note that each of these binarized codewords are
  uniquely decodeable).

MVDx Binarization
0 0
1 10
2 110
3 1110
4 11110
5 111110
6 1111110
7 11111110
8 111111110
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• Choose a context model for each bin. One of 3
  models is selected for bin 1, based on previous
  coded MVD values. The L1 norm of two
  previously-coded values, ek, is calculated
• ekMVDAMVDB
• A left block, B above block
• If ek is small, then there is a high probability
  that the current MVD will have a small magnitude
  if ek is large then it is more likely that the
  current MVD will have a large magnitude. We
  select a probability table (context model)
  accordingly.

ek Context model for bin 1
0 lt ek lt 3 Model 0
3 lt ek lt 32 Model 1
32 lt ek Model 2
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• The remaining bins are coded using one of 4
  further context models
• Encode each bin. The selected context model
  supplies two probability estimates the
  probability that the bin contains 1 and the
  probability that the bin contains 0.
• These estimates determine the two sub-ranges that
  the arithmetic coder uses to encode the bin.

Bin Context mode
1 0, 1, or 2 (depend on ek)
2 3
3 4
4 5
5 6
6 and higher 7
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• Update the context models. For example, if
  context model 2 was selected for bin 1 and the
  value of bin 1 was 0, the frequency count of
  0s is incremented. This means that the next
  time this model is selected, the probability of
  an 0 will be slightly higher.
• When the total number of occurrences of a model
  exceeds a threshold value, the frequency counts
  for 0 and 1 will be scaled down, which in
  effect gives higher priority to recent
  observations.
• At the beginning of each coded slice, the context
  models are initialized depending on the initial
  value of the Quantization Parameter QP (since
  this has a significant effect on the probability
  of occurrence of the various data symbols).

55
The arithmetic coding engine

• The arithmetic decoder has three distinct
  properties
• Probability estimation is performed by a
  transition process between 64 separate
  probability states for Least Probable Symbol
  (LPS, the least probable of the two binary
  decisions 0 or 1).
• The range R representing the current state of the
  arithmetic coder is quantized to a small range of
  pre-set values before calculating the new range
  at each step, making it possible to calculate the
  new range using a look-up table (i.e.
  multiplication-free).
• A simplified encoding and decoding process is
  defined for data symbols with a near-uniform
  probability distribution. (bypass)
• The definition of the decoding process is
  designed to facilitate low-complexity
  implementations of arithmetic encoding and
  decoding.

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