An introduction to Data Compression

An introduction toData Compression

Gabriele Monfardini - Corso di Basi di Dati Multimediali a.a. 2005-2006 2

General informations Requirements

some programming skills (not so much...) knowledge of data structures ... some work!

Office hours ...... please write me an email [email protected]

mailto:[email protected]?subject=Corso%20di%20Basi%20di%20Dati%20Multimediali


What is compression? Intuitively compression is a method “to

press something into a smaller space”. In our domains a better definition is “to

make information shorter”


Some basic questions What is information? How can we measure the amount of

information? Why compression is useful? How do we compress? How much we can compress?


What is information? - I Commonly the term information refers to the

knowledge of some fact, circumstance or thought.

For example we can think about reading a newspaper, news are the information. syntax

letters, punctuation marks, white spaces, grammar rules ...

semantics meaning of the words and of the sentences


What is information? - II In our domain, information is merely

the syntax, i.e. we are interested in the symbols of the alphabet used to express the information.

In order to give a mathematical definition of information we need some principle of Information Theory


The fundamental concept A key concept in Information Theory is that

the information is conveyed by randomness Which information give us a biased coin, which

outcome is always head? What about another biased coin, which outcome is

head with 90% probability?

We need a way to measure quantitatively the amount of information in some mathematical sense


The Uncertainty - I Suppose we have a discrete random

variable and is a particular outcome with probability

uncertainty The units are given by the base of the

logarithms base 2 bits base 10 nats

X x( )p x

log( ( )) log(1 ( ))p x p x


The Uncertanty - II Suppose the random variable output

each outcome has 1 bit of information

0 gives no information at all, while if the outcome is 1 the information is

0,1

(0) (1) 0.5p p

(0) 1, (1) 0p p


The Entropy More useful is the entropy of a random

variable with values in a space

The entropy is a measure of the average uncertanty of the random variable

X X( ) [uncertanty] ( ) log( ( ))

x

H X E p x p x

X


The entropy - examples Consider again a r.v. with only two possible

outcomes, 0 and 1In this case

( ) (0.5log 0.5 0.5log 0.5) 1 bitH p

( ) (0.9 log 0.9 0.1log 0.1) 0.469 bitsH p

1 with prob ( )

0 with prob 1p

X H pp

0.5p

0.9p


Compression and loss lossless

decompressed message (file) is an exact copy of the original. Useful for text compression

lossy some information is lost in the decompressed

message (file). Useful for image and sound compression

lgnore for a while lossy compression


Definitions - I A source code from a r.v. is a

mapping from to , the set of finite-length string from a D-ary alphabet.

, codeword for , length of

XX *̂D

( )C x( )l x

x( )C x


Definitions - II non-singular code (... trivial ...)

every element of is mapped in a different string of :

extension of a code

uniquely decodable code its extension is uniquely decodable

X*D ( ) ( )i j i jx x C x C x

*C C1 2 1 2( ... ) ( ) ( )... ( )n nC x x x C x C x C x


Definitions - III prefix (better prefix-free) or

istantaneous code no codeword is a prefix of any other codeword the advantage is that decoding has no need to look-

ahead

codewords

a 11 b 110

X ... 11? ...


ExamplesX Code 1 Code 2 Code 3 Code 4

1 01 0 10 02 110 010 00 103 010 10 11 1104 110 01 110 111

singularnot singular, but not uniquely decodableuniquely decodable, but not instantaneousinstantaneous


Kraft Inequality - I Theorem (Kraft Inequality)

For any instantaneous code over an alphabet of size D, the codeword lengths must satisfy

Conversely, given a set of codeword lengths that satisfy this inequality there exists an istantaneous code with these word lengths

1 2, ,..., ml l l

1

1i

ml

i

D


Kraft Inequality - II Consider a complete D-ary tree

at level k, there are nodes a node at level has descendants that

are nodes at level k

kDp k k pD

level 0

level 1

level 2

level 3


Kraft Inequality - III Proof

Consider a D-ary tree (not necessarily complete) representing the codewords, each path down the tree is a sequence of symbols, and each leaf (with its unique path) is a codeword. Let be the longest codeword.A codeword of length , being a leaf, imply that at level there are missing nodes

maxl

maxil l

maxl max il lD


Kraft Inequality - IVThe total number of possible nodes at level is Summing over all codewords

Dividing by

maxlmaxlD

max max-

1

i

ml l l

i

D D

maxlD

1

1i

ml

i

D


Kraft Inequality - V Proof

Suppose (without loss of generality) that codewords are ordered by length, i.e. .Consider a D-ary tree and start assigning each codeword to a node, starting from .For a generic codeword with length consider the set K of codewords with length , except i. Suppose there is no available node at level i. That is,

ilk il l

i k il l l

k K

D D

1 2 ... ml l l

1l


Kraft Inequality - VIbut this means that

Then

that is absurd. Then the obtained tree represents an instantaneous code with desidered codeword lengths

1kl

k K

D

1jl

j K i

D


Models and coders

The model supplies the probabilities of the symbols (or of the group of symbols, as we will see later)

The coder encodes and decodes starting from these probabilities

model model

encoder decodertext textcompressed text


Good modeling is crucial What happens if the true probability of

the symbols to be coded are but we use ?

Simply, compressed text will be longer, i.e. the average number of bits/symbol will be greater

It is possible to calculate the difference in bit/symbol from the two mass probability p and q, known as relative entropy

ip iq


Finite-context models in english text ...... but

A finite-context model of order m uses the previous m symbols to make the prediction

Better modeling but we need to extimate much more probabilities

( ) 0.02ip x u

1( | ) 0.95i ip x u x q


Finite-state models

Although potentially more powerful (e.g. they can model wheather an odd or even number of as have occurred consecutively), they are not so popular.

Obviously the decoder uses the same model, so they are always in the same states

1 2a 0.5

a 0.99b 0.01b 0.5


Static models A models is static if we set up a

reasonable probability distribution and use it for all the texts to be coded.

Poor performance in case of different kind of sources (english text, financial data...)

One solution is to have K different models and to send the index of the used model

... but cfr. the book Gadsby by E. V. Wright


Adaptive models In order to solve the problems of static

modeling, adaptive (or dynamic) models begin with a bland probability distribution, that is refined as more symbols of the text are known

The encoder and the decoder have the same initial distribution, and the same rules to alter it

There could be adaptive models of order m>0


The zero-frequency problem The situation in which a symbol is predicted

with probability zero should be avoided, as it cannot be coded

One solution: the total number of symbols in the text is increased by 1. This 1/total probability is divided among all unseen symbols

Another solution: to augment by 1 the count of every symbol

Many more solutions... Which is the best? If text is sufficiently long the

compression is similar


Symbolwise and dictionary models The set of all possible symbols of a source is

called the alphabet Symbolwise models provide an extimated

probability for each symbol in the alphabet Dictionary models instead replace substrings

in a text with codewords that identify each substring in a collection, called dictionary or codebook

An introduction to Data Compression

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