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modeling. As an important design decision, the latter case is generally applied to
the most frequently observed bins only, whereas the other, usually less frequently
observed bins, will be treated using a joint, typically zero-order probability model.
In this way, CABAC enables selective adaptive probability modeling on a sub-
symbol level, and hence, provides an efficient instrument for exploiting inter-symbol
redundancies at significantly reduced overall modeling or learning costs. Note that
for both the fixed and the adaptive case, in principle, a switch from one probability
model to another can occur between any two consecutive regular coded bins. In
general, the design of context models in CABAC reflects the aim to find a good
compromise between the conflicting objectives of avoiding unnecessary modeling-
cost overhead and exploiting the statistical dependencies to a large extent.
The parameters of probability models in CABAC are adaptive, which means
that an adaptation of the model probabilities to the statistical variations of the
source of bins is performed on a bin-by-bin basis in a backward-adaptive and
synchronized fashion both in the encoder and decoder; this process is called
probability estimation
. For that purpose, each probability model in CABAC can take
one out of 126 different states with associated model probability values p ranging
in the interval Œ0:01875; 0:98125. The two parameters of each probability model
are stored as 7-bit entries in a context memory: 6 bits for each of the 63 probability
states representing the model probability p
LPS
of the least probable symbol (LPS)
and 1 bit for
MPS
, the value of the most probable symbol (MPS). The probability
estimator in CABAC is based on a model of “exponential aging” with the following
recursive probability update after coding a bin b at time instance t :
D
(
˛
p
.t /
LPS
;
if b
D
MPS
; i.e., an MPS occurs
p
.t C1/
LPS
(8.1)
1
˛
.1
p
.t /
LPS
/;
otherwise.
Here, the choice of the scaling factor ˛ determines the speed of adaptation: A value
of ˛ close to 1 results in a slow adaptation (“steady-state behavior”), while faster
adaptation can be achieved for the non-stationary case with decreasing ˛.Note
that this estimation is equivalent to using a sliding window technique [
4
,
65
] with
window size W
˛
D
.1
˛/
1
. In the design of CABAC, Eq. (
8.1
) has been used
together with the choice of
˛
D
0:01875
0:5
1
63
p
.t /
with min
t
LPS
D
0:01875;
(8.2)
and a suitable quantization of the underlying LPS-related model probabilities into
63 different states, to derive a finite-state machine (FSM) with tabulated transition
rules [
46
]. This table-based probability estimation method was unchanged in HEVC,
although some proposals for alternative probability estimators [
1
,
78
]haveshown
average bit rate savings of 0.8-0.9 %, albeit at higher computational costs.
Each probability model in CABAC is addressed using a unique context index
(ctxIdx), either determined by a fixed assignment or computed by the context
