The document provides lecture notes on arithmetic coding for data compression, covering topics such as arithmetic coding encoding and decoding algorithms, comparing arithmetic coding to Huffman coding, dictionary techniques like Lempel-Ziv coding, and applications of lossless compression techniques. Arithmetic coding assigns a unique identifier or tag to a sequence and then gives that tag a unique binary code, while dictionary techniques code each symbol or group of symbols with an element from a static or dynamic dictionary.

1. Lecture Notes on Arithmetic Coding
for
Open Educational Resource
on
Data Compression(CA209)
by
Dr. Piyush Charan
Assistant Professor
Department of Electronics and Communication Engg.
Integral University, Lucknow
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

2. UNIT-III Syllabus
• Arithmetic Coding: Coding a sequence,
• Generating a Binary code,
• Comparison of Arithmetic and Huffman coding.
• Dictionary Techniques: Introduction, Static Dictionary:
• Diagram Coding, Adaptive Dictionary:
• The LZ77 Approach, The LZ78 Approach.
• Applications: File Compression, Image Compression
• Lossless Image Compression: Multi-resolution Approaches.
• Context Based Compression: Dynamic Markov Compression.
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3.  Coding rate is the average number of bits used to represent a symbol from a source.
 For a given probability model, the entropy is the lowest rate at which the source can
be coded.
 Huffman coding will generate whose rate is within p_max + 0. 086
 Therefore, in Huffman coding, when the alphabet size is large, the amount of
deviation from the entropy is quite small, and vice versa.
 One solution for this problem is blocking in Huffman coding. In which, it is more
efficient to generate codewords for groups or sequences of symbols rather than to
generate a separate codeword for each symbol in a sequence.
 In order to find the Huffman coding for a sequence of length m, we need
codewords for all possible sequences of length m.
 This causes an exponential growth in the size of the code book.
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4.  We need a way of assigning codewords to particular sequences with out having to
generate a codes for all sequences of that length.
 Rather than separating the input into component symbols and replacing each with a code,
arithmetic encodes the entire message with a number (tag).
 Firstly, a unique identifier or tag is generated for a sequence. Secondly, this tag is then
given a unique binary code.
• Entropy encoding • Lossless data compression • Variable length coding
Arithmetic Coding
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5. Arithmetic Coding
 Arithmetic coding is based on the concept of interval subdividing.
– In arithmetic coding a source ensemble is represented by an interval between 0
and 1 on the real number line.
– Each symbol of the ensemble narrows this interval.
– As the interval becomes smaller, the number of bits needed to specify it grows
– Arithmetic coding assumes an explicit probabilistic model of the source.
– It uses the probabilities of the source messages to successively narrow the
interval used to represent the ensemble.
 A high probability message narrows the interval less than a low
probability message, so that high probability messages contribute fewer
bits to the coded ensemble.
4/22/2021 Dr. Piyush, Charan Dept. of ECE, Integral University, Lucknow 5

6.  Assume we know the probabilities of each symbol of the data source,
 we can allocate to each symbol an interval with width proportional to its
probability, and each of the intervals does not overlap with others.
 This can be done if we use the cumulative probabilities as the two ends of each
interval.
 Therefore, the two ends of each symbol x amount to Q[x-1] and Q[x].
 Symbol x is said to own the range [Q[x-1], Q[x]).
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 We begin with the interval [0, 1) and subdivide the interval iteratively.
 For each symbol entered, the current interval is divided according to the
probabilities of the alphabet.
 The interval corresponding to the symbol is picked as the interval to be further
proceeded with.
 The procedure continues until all symbols in the message have been processed.
 Since each symbol's interval does not overlap with others, for each possible
message there is a unique interval assigned.
 We can represent the message with the interval's two ends [L, H). In fact, taking
any single value in the interval as the encoded code is enough, and usually the
left end L is selected.

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11. Once the character probabilities are
known, the individual symbols need
to be assigned a range along a
"probability line," which is nominally
0 to 1. It doesn't matter which
characters are assigned which
segment of the range, as long as it is
done in the same manner by both
the encoder and the decoder. The
nine-character symbol set used here
would look like Figure 2.
Dr. Piyush, Charan Dept. of ECE, Integral University, Lucknow 11
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12. Each character is assigned the
portion of the 0 - 1 range that
corresponds to its probability of
appearance. Note also that the
character "owns" everything up
to, but not including the higher
number. So the letter T in fact
has the range 0.90 - 0.9999 ....
Dr. Piyush, Charan Dept. of ECE, Integral University, Lucknow 12
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13. After the first character is encoded, we
also know that the range for our output
number is bounded by the low and high
numbers. During the rest of the encoding
process, each new symbol to be encoded
will further restrict the possible range of
the output number. The next character to
be encoded, I, owns the range 0.50
through 0.60. If this was the first number
in our message, we would set these as our
low- and high-range values. But I is the
second character; therefore, we say that I
owns the range corresponding to 0.50 -
0.60 in the new subrange of 0.2 - 0.3. This
means that the new encoded number will
have to fall somewhere in the 50 to 60th
percentile of the currently established
range.
Dr. Piyush, Charan Dept. of ECE, Integral University, Lucknow 13
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14. Dr. Piyush, Charan Dept. of ECE, Integral University, Lucknow 14
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15. Binary Codeword
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16. Decoding Algorithm
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17. Decoding BILL GATES
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18. Dr. Piyush, Charan Dept. of ECE, Integral University, Lucknow 18
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19. Huffman vs. Arithmetic Codes
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20. Dr. Piyush, Charan Dept. of ECE, Integral University, Lucknow 20
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21. Dr. Piyush, Charan Dept. of ECE, Integral University, Lucknow 21
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Huffman vs. Arithmetic Codes

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24. Arithmetic Coding Huffman Coding
Does not need the probability distribution Need a probability distribution
No need to keep and send codeword table Need to store the codeword table
Decompression speed is slow Decompression speed is Fast
Compression Speed is low Compression speed is Fast
Compression ratio is very good Compression ratio is poor
No compressed pattern matching Compressed pattern matching
Fractional codeword length Minimum codeword length is 1 bit
Does not produce Prefix code Produce Prefix code
Comparison of Arithmetic vs. Huffman
Coding
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25. Dr. Piyush, Charan Dept. of ECE, Integral University, Lucknow 25
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29.  each symbol or group of symbols is encoded with a variable
length code, according to some probability distribution.
 based on the use of a dictionary, which can be static or dynamic, and
they code each symbol or group of symbols with an element of the
dictionary.
Huffman
Dynamic Markov Compression
Lempel-Ziv-Welch
Lossless Compression Techniques
4/22/2021 Dr. Piyush, Charan Dept. of ECE, Integral University, Lucknow 29

30. Lempel-Ziv-Welch (LZW)
Created by Abraham Lempel, Jacob Ziv, and Terry Welch. It was published by Welch in
1984as an improved implementation of the LZ78 algorithm, published by Lempel and Ziv
in 1978
universal adaptative1 lossless data compression algorithm
builds a translation table (also called dictionary) from the text being compressed
the string translation table maps the message strings to fixed-length codes
1
The coding scheme used for the kth
character of a message is based on the characteristics of the preceding k −
1 characters in the message
Dr. Piyush, Charan Dept. of ECE, Integral University, Lucknow 30
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31. Dictionary Based Techniques
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32. Lempel –Ziv Coding
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33. Dr. Piyush, Charan Dept. of ECE, Integral University, Lucknow 33
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Lempel –Ziv Coding

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37. Dr. Piyush, Charan Dept. of ECE, Integral University, Lucknow 37
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38. Lempel-Ziv-Welch (LZW) Compression Algorithm
 As mentioned earlier, static coding schemes require some knowledge
about the data before encoding takes place.
 Universal coding schemes, like LZW, do not require advance
knowledge and can build such knowledge on-the-fly.
 LZW is the foremost technique for general purpose data compression
due to its simplicity and versatility.
 It is the basis of many PC utilities that claim to “double the capacity of
your hard drive”
 LZW compression uses a code table, with 4096 as a common choice for
the number of table entries.
Dr. Piyush, Charan Dept. of ECE, Integral University, Lucknow 38
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39. LZW (cont'd)
 Codes 0-255 in the code table are always assigned to represent single bytes
from the input file.
 When encoding begins the code table contains only the first 256 entries,
with the remainder of the table being blanks.
 Compression is achieved by using codes 256 through 4095 to represent
sequences of bytes.
 As the encoding continues, LZW identifies repeated sequences in the data,
and adds them to the code table.
 Decoding is achieved by taking each code from the compressed file, and
translating it through the code table to find what character or characters it
represents.
Dr. Piyush, Charan Dept. of ECE, Integral University, Lucknow 39
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40. LZW Encoding Algorithm
1 Initialize table with single character strings
2 P = first input character
3 WHILE not end of input stream
4 C = next input character
5 IF P + C is in the string table
6 P = P + C
7 ELSE
8 output the code for P
9 add P + C to the string table
10 P = C
11 END WHILE
12 output code for P
Dr. Piyush, Charan Dept. of ECE, Integral University, Lucknow 40
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41. Example 1: Compression using LZW
Example 1: Use the LZW algorithm to compress the string
BABAABAAA
Dr. Piyush, Charan Dept. of ECE, Integral University, Lucknow 41
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42. Example 1: LZW Compression Step 1
BABAABAAA P=A
C=empty
Dr. Piyush, Charan Dept. of ECE, Integral University, Lucknow 42
STRING TABLE
ENCODER OUTPUT
string
codeword
representing
output code
BA
256
B
66
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43. Example 1: LZW Compression Step 2
BABAABAAA P=B
C=empty
Dr. Piyush, Charan Dept. of ECE, Integral University, Lucknow 43
STRING TABLE
ENCODER OUTPUT
string
codeword
representing
output code
BA
256
B
66
AB
257
A
65
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44. Example 1: LZW Compression Step 3
BABAABAAA P=A
C=empty
Dr. Piyush, Charan Dept. of ECE, Integral University, Lucknow 44
STRING TABLE
ENCODER OUTPUT
string
codeword
representing
output code
BA
256
B
66
AB
257
A
65
BAA
258
BA
256
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45. Example 1: LZW Compression Step 4
BABAABAAA P=A
C=empty
Dr. Piyush, Charan Dept. of ECE, Integral University, Lucknow 45
STRING TABLE
ENCODER OUTPUT
string
codeword
representing
output code
BA
256
B
66
AB
257
A
65
BAA
258
BA
256
ABA
259
AB
257
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46. Example 1: LZW Compression Step 5
BABAABAAA P=A
C=A
Dr. Piyush, Charan Dept. of ECE, Integral University, Lucknow 46
STRING TABLE
ENCODER OUTPUT
string
codeword
representing
output code
BA
256
B
66
AB
257
A
65
BAA
258
BA
256
ABA
259
AB
257
AA
260
A
65
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47. Example 1: LZW Compression Step 6
BABAABAAA P=AA
C=empty
Dr. Piyush, Charan Dept. of ECE, Integral University, Lucknow 47
STRING TABLE
ENCODER OUTPUT
string
codeword
representing
output code
BA
256
B
66
AB
257
A
65
BAA
258
BA
256
ABA
259
AB
257
AA
260
A
65
AA
260
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48. LZW Decompression
 The LZW decompressor creates the same string table during
decompression.
 It starts with the first 256 table entries initialized to single characters.
 The string table is updated for each character in the input stream, except
the first one.
 Decoding achieved by reading codes and translating them through the
code table being built.
Dr. Piyush, Charan Dept. of ECE, Integral University, Lucknow 48
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49. LZW Decompression Algorithm
1 Initialize table with single character strings
2 OLD = first input code
3 output translation of OLD
4 WHILE not end of input stream
5 NEW = next input code
6 IF NEW is not in the string table
7 S = translation of OLD
8 S = S + C
9 ELSE
10 S = translation of NEW
11 output S
12 C = first character of S
13 OLD + C to the string table
14 OLD = NEW
15 ENDWHILE
Dr. Piyush, Charan Dept. of ECE, Integral University, Lucknow 49
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50. Example 2: LZW Decompression 1
Example 2: Use LZW to decompress the output sequence of
Example 1:
<66><65><256><257><65><260>.
Dr. Piyush, Charan Dept. of ECE, Integral University, Lucknow 50
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51. Example 2: LZW Decompression Step 1
<66><65><256><257><65><260> Old = 65 S = A
New = 66 C = A
Dr. Piyush, Charan Dept. of ECE, Integral University, Lucknow 51
STRING TABLE
ENCODER OUTPUT
string
codeword
string
B
BA
256
A
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52. Example 2: LZW Decompression Step 2
<66><65><256><257><65><260> Old = 256 S = BA
New = 256 C = B
Dr. Piyush, Charan Dept. of ECE, Integral University, Lucknow 52
STRING TABLE
ENCODER OUTPUT
string
codeword
string
B
BA
256
A
AB
257
BA
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53. Example 2: LZW Decompression Step 3
<66><65><256><257><65><260> Old = 257 S = AB
New = 257 C = A
Dr. Piyush, Charan Dept. of ECE, Integral University, Lucknow 53
STRING TABLE
ENCODER OUTPUT
string
codeword
string
B
BA
256
A
AB
257
BA
BAA
258
AB
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54. Example 2: LZW Decompression Step 4
<66><65><256><257><65><260> Old = 65 S = A
New = 65 C = A
Dr. Piyush, Charan Dept. of ECE, Integral University, Lucknow 54
STRING TABLE
ENCODER OUTPUT
string
codeword
string
B
BA
256
A
AB
257
BA
BAA
258
AB
ABA
259
A
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55. Example 2: LZW Decompression Step 5
<66><65><256><257><65><260> Old = 260 S = AA
New = 260 C = A
Dr. Piyush, Charan Dept. of ECE, Integral University, Lucknow 55
STRING TABLE
ENCODER OUTPUT
string
codeword
string
B
BA
256
A
AB
257
BA
BAA
258
AB
ABA
259
A
AA
260
AA
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56. LZW: Some Notes
 This algorithm compresses repetitive sequences of data well.
 Since the codewords are 12 bits, any single encoded character will expand
the data size rather than reduce it.
 In this example, 72 bits are represented with 72 bits of data. After a
reasonable string table is built, compression improves dramatically.
 Advantages of LZW over Huffman:
 LZW requires no prior information about the input data stream.
 LZW can compress the input stream in one single pass.
 Another advantage of LZW its simplicity, allowing fast execution.
Dr. Piyush, Charan Dept. of ECE, Integral University, Lucknow 56
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57. LZW: Limitations
 What happens when the dictionary gets too large (i.e., when all the 4096 locations have
been used)?
 Here are some options usually implemented:
 Simply forget about adding any more entries and use the table as is.
 Throw the dictionary away when it reaches a certain size.
 Throw the dictionary away when it is no longer effective at compression.
 Clear entries 256-4095 and start building the dictionary again.
 Some clever schemes rebuild a string table from the last N input characters.
Dr. Piyush, Charan Dept. of ECE, Integral University, Lucknow 57
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58. Lossless Image Compression: Multi-resolution
Approaches.
Image compression is a type of data compression applied to digital images, to reduce their cost
for storage or transmission.
Image compression may be lossy or lossless. Lossless compression is preferred for archival purposes and
often for medical imaging, technical drawings, clip art, or comics.
Methods for lossless compression:
Run-length encoding – used in default method in PCX and as one of possible in BMP, TGA, TIFF
Area image compression
Predictive coding – used in DPCM
Entropy encoding – the two most common entropy encoding techniques are arithmetic coding and Huffman
coding
Adaptive dictionary algorithms such as LZW – used in GIF and TIFF
DEFLATE – used in PNG, MNG, and TIFF
Chain codes
Dr. Piyush, Charan Dept. of ECE, Integral University, Lucknow 58
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59. Context Based Compression: Dynamic Markov
Compression.
 developed by Gordon Cormack and Nigel Horspool (1987)
 adaptative lossless data compression algorithm
 based on the modelization of the binary source to be encoded by means of a Markov chain,
which describes the transition probabilities between the symbol “0” and the symbol “1”
 the built model is used to predict the future bit of a message. The predicted bit is then coded
using arithmetic coding
Dynamic Markov compression (DMC) is a lossless data compression algorithm developed by Gordon
Cormack and Nigel Horspool.It uses predictive arithmetic coding similar to prediction by partial
matching (PPM), except that the input is predicted one bit at a time (rather than one byte at a time).
DMC has a good compression ratio and moderate speed, similar to PPM, but requires somewhat more
memory and is not widely implemented. Dynamic Markov Compression is an obscure form of
compression that uses Markov chains to model the patterns represented in a file.
Dr. Piyush, Charan Dept. of ECE, Integral University, Lucknow 59
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60. Each circle represents a state, and each arrow
represents a transition. In this example, we have
two states, raining and sunny, a perfect
representation of true weather. Each state has
two possible transitions, it can transition to itself
again or it can transition to another state. The
likelihood of each transition is defined by a
percentage representing the probability that the
transition occurs.
Now let’s say it’s sunny and we’re following this
model. According to the model there’s a 50%
chance it’s sunny again tomorrow or a 50%
chance it’s rainy tomorrow. If it becomes rainy,
then there’s a 25% chance it’s rainy the day after
that or a 75% chance it’s sunny the day after that.
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