Transcript VLC - National Tsing Hua University

Variable Length Coding
 Information entropy

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Huffman code vs. arithmetic code
Arithmetic coding
Why CABAC?
Rescaling and integer arithmetic coding
Golomb codes
Binary arithmetic coding
CABAC
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Information Entropy
 Information entropy: Claude E. Shannon 1948, “A Mathematical
Theory of Communication”
 The information contained in a statement asserting the occurrence of
an event depends on the probability p(f), of occurrence of the event f.
- lg p(f)
 The unit of the above informationp quantity is referred as a bit, since
it is the amount of information carried by one (equally likely) binary
digit.
 Entropy H is a measure of uncertainty or information content
H    f A p( f ) lg p( f )
- Very uncertain  high information content
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Entropy Rate
 Conditional entropy H(F|G) between F and G: uncertainty of F given
G
H ( F | G )  gA pG ( g ) H ( F | g )
g
 Nth order entropy
H N ( F )  H ( F1,...,FN )
 Mth order conditional entropy
HC ,N ( F )  H ( FM 1 | FM ,...,F1 )
 Entropy rate (lossless coding bound)
1
H N  lim H C , N
N  N
N 
H  lim
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Bound for Lossless Coding
 Scalar coding: could differ from the entropy by up to 1 bit/symbol
H1  R1  H1  1
 Vector (block) coding: assign one codeword for each group of N
symbols
H N  RN  H N  1
H N / N  RN  H N / N  1/ N
lim R N  H
N 
 Conditional coding (predictive coding, context-based coding): The
codeword of the current symbol depends on the pattern (context)
formed by the previous M symbol
H  lim RC ,M  H  1
M 
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Huffman Coding
• Huffman coding for pdf: (a1, a2, a3) = (0.5, 0.25, 0.25)
– –lg0.5 = 1, –lg 0.25 = 2
a1 = 0.5
0
0
a2 = 0.25
1
a3 = 0.25
1
– a1 = 0, a2 = 10, a3 = 11
• If the self information is not integer?
– pdf: (a1, a2, a3, a4) = (0.6, 0.2, 0.125, 0.075)
– –lg 0.6 = 0.737, –lg 0.2 = 2.32,
0
–lg 0.125 = 3, –lg 0.075 = 3.74
1
a1 = 0.6
1
– a1 = 0, a2 = 10, a3 = 110, a4 = 111
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a2 = 0.2
0
0
a3 = 0.125
1
a4 = 0.075
H1=1.5617
R1=1.6
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Huffman vs. Arithmetic Coding
 Huffman coding: convert a fixed number of symbols into a variable
length codeword
 Efficiency
H N / N  R  H N / N  1/ N
 The usage of fixed VLC tables does not allow an adaptation to the actual
symbol statistics.
 Arithmetic Coding: convert a variable number of symbols into a
variable length codeword
 Efficiency
HN / N  R  HN / N  2 / N
 Process one symbol at a time
 Easy to adapt to changes in source statistics
 Integer implementation is available
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Arithmetic Coding
• The bits allocated for each symbol can be non-integer
– If pdf(a) = 0.6, then the bits to encode ‘a’ is 0.737
• For the optimal pdf, the coding efficiency is always better than or
equal to the Huffman coding
• Huffman coding for a2 a1 a4 a1 a1 a3, total 11 bits:
2 + 1 +
3
+ 1 + 1 +
3
• Arithmetic coding for a2 a1 a4 a1 a1 a3, total 11.271 bits:
2.32
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+0.737+
3.74
+0.737+0.737+
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The exact probs are preserved
Arithmetic Coding
• Basic idea:
– Represent a sequence of symbols by
an interval with length equal to its
probability
– The interval is specified by its lower
boundary (l), upper boundary (u)
and length d (= probability)
– The codeword for the sequence is the
common bits in binary
representations of l and u
• The interval is calculated
sequentially starting from the first
symbol
– The initial interval is determined by
the first symbol
– The next interval is a subinterval of
the previous one, determined by the
next symbol
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al , l  1, 2, ..., L
pl  p(al )
l
ql   pl , thecumulat iveprobability up to thelth symbol
k 1
l 0  0, u 0  1, and d 0  1
On receiving the nth sym bol, al
d n  d n 1  pl ; l n  l n 1  d n 1  ql 1 ; u n  l n  d n
l n  l n 1  (u n 1  l n 1 )  ql 1

 u n  l n 1  (u n 1  l n 1 )  ql
u n  l n1  d n1ql 1  d n1 pl 
l n1  d n1 (ql 1  pl )
x n : thenth observed symbol
l ( n )  l ( n 1)  (u ( n 1)  l ( n 1) )  FX ( x n  1)
 (n)
( n 1)
 (u ( n 1)  l ( n 1) )  FX ( x n )
 u l
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binary value between l and u can
An Example Any
unambiguously specify the input message.
½=(10…)=(01…1…)
¼ =(010…)=(001…1…)
d(ab)=1/8
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Why CABAC?

The first standard that uses arithmetic entropy coder is given by
Annex E of H.263
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Drawbacks:
1. Annex E is applied to the same syntax elements as the VLC
elements of H.263
2. All the probability models are non-adaptive that their
underlying probability as assumed to be static.
3. The generic m-ary arithmetic coder used involves a
considerable amount of computational complexity.
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CABAC: Technical Overview
update probability estimation
Context
modeling
Binarization
Probability
estimation
Coding
engine
Adaptive binary arithmetic coder
Maps non-binary
Chooses a model
conditioned on past symbols to a binary
sequence
observations
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Uses the provided model
for the actual encoding
and updates the model
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CABAC
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Context-based Adaptive Binary Arithmetic Code
(CABAC)
 Usage of adaptive probability models
 Exploiting symbol correlations by using contexts
 Non-integer number of bits per symbol by using
arithmetic codes
 Restriction to binary arithmetic coding
• Simple and fast adaptation mechanism
• But: Binarization is needed for non-binary symbols
• Binarization enables partitioning of state space
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Implementation of Arithmetic Coding
 Rescaling and Incremental coding
 Integer arithmetic coding
 Binary arithmetic coding
 Hoffman Trees
 Exp-Golomb Codes
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Issues

Finite precision (underflow & overflow): As n gets larger, these
two values, l(n) and u(n) come closer and closer together. This
means that in order to represent all the subintervals uniquely we
need to increase the precision as the length of the sequence
increases.
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Incremental transmission: transmit portions of the code as the
sequence is being observed.
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Integer implementation
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Rescaling & Incremental Coding
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l ( n)  l ( n1)  (u ( n1)  l ( n1) )  FX ( xn  1)
u ( n)  l ( n1)  (u ( n1)  l ( n1) )  FX ( xn )
Incremental Encoding
U
U
L
L
L
U
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Question for Decoding

How do we start decoding? decode the first symbol
unambiguously
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How do we continue decoding? mimic the encoder
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How do we stop decoding?
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Incremental Decoding
U
= 0.312+(0.6-0.312)0.8
= 0.312+(0.6-0.312)0.82
1.0
Top 18% of [0,0.8)
0.82
0.8
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Issues in the Incremental Coding
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Solution
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Solution (2)
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Incremental Encoding
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Incremental Decoding
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Integer Implementation
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Integer Implementation
• nj: the # of times the symbol j
occurs in a sequence of length
Total Count.
k
n
• FX(k) can be estimated by FX (k )  i 1 i
TotalCount
k
• Define Cum _ Count  i 1 ni
we have
 (u ( n 1)  l ( n 1)  1)  Cum _ Count( xn  1) 
l l


TotalCount


 (u ( n 1)  l ( n 1)  1)  Cum _ Count( xn ) 
(n)
( n 1)
u l

 1
TotalCount


(n)
( n 1)
• E3: if (E3 holds)
Shift l to the left by 1 and shift 0 into LSB
Shift u to the left by 1 and shift 0 into LSB
Complement (new) MSB of l and u
Increment Scale3
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Golomb Codes
 Golomb-Rice code: a family of codes designed to encode integers with
the assumption that the larger an integer, the lower its probability of
occurrence.
 An example (the simplest, unary code): for integer n, codes as n 1s
followed by a 0. This code is the same as the Huffman code for {1, 2,
1
…} with probability model p[k ]  2 .

n
 q 

 Golomb code with m: code n > 0 using two numbers q and r: r  nmqm
 Q is coded by unary code of q; r is represented by binary code using lg m
bits.
n q r code n q r code
 the first 2 lg m  – m values, uses lg m bits
0 0 0 000
5 1 0 1000
 the rest values: r  2lgm  m use lg m + 1bits 1 0 1 001 6 1 1 1001
2 0 2 010
7 1 2 1010
 Golomb code for m = 5:
3 0 3 0110 8 1 3 10110
k
3+3=110
4 0 4 0111
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1 4 10111
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Golomb Codes
 Golomb code is optimal for the probability model
P( n )  p n 1q,
q  1 p
 1 
m  
.

 lg p 
 exp-Golomb code: variable length codes with regular
construction: [m zeros] [1] [info].
code_num: index
info: is an m-bit field carrying information
 Mapping types: ue, te, se, and me
 designed to produce short codewords for frequently-occurring values and
longer codewords for less common parameter values.
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exp-Golomb Codes
 exp-Golomb code
m  lg( code _ num  1)
the group size
increases exponentially
info  code_num  1  2m

Decode:
1. Read in m leading zeros followed by 1.
2. Read m -bit info field.
21-2
3. code_num  2m  info  1
﹝m zeros﹞﹝1﹞﹝info﹞
22-2
23-2
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exp-Golomb Entropy Coding
A parameter k to be encoded is mapped to code_num in one
of the following ways:
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exp-Golomb Entropy Coding
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H.264 Coding Parameters
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Uniqueness and Efficiency of AC(1)
-log2 p(x)
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Uniqueness and Efficiency of AC(2)
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Uniqueness and Efficiency of AC(3)
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Uniqueness and Efficiency of AC(4)
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