# Lecture 3: Source Coding Theory TSBK01 Image Coding and Data Compression Jörgen Ahlberg Div. of Sensor Technology Swedish Defence Research Agency (FOI)

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Lecture 3: Source Coding Theory TSBK01 Image Coding and Data Compression Jörgen Ahlberg Div. of Sensor Technology Swedish Defence Research Agency (FOI)

Outline 1.Coding & codes 2.Code trees and tree codes 3.Optimal codes  The source coding theorem

Part 1: Coding & codes  Coding: To each sorce symbol (or group of symbols) we assign a codeword from an extended binary alphabet.  Types of codes: –FIFO: Fixed input (i.e., # source symbols, fixed output (i.e., # code symbols). –FIVO:Fixed input, variable output. –VIFO:Variable input, fixed output. –VIVO:Variable input, variable output.  FIVO and VIVO are called variable length codes (VLC). Should be comma-free.

Example  Assume a memoryless source with alphabet A = {a 1, …, a 4 } probabilities P(a 1 ) = ½ P(a 2 ) = ¼ P(a 3 ) = P(a 4 ) = 1/8.  a 1 FIFO: 00 FIVO: 0 a 2 0101 a 3 10110 a 4 11111

All codes Non-singular codes Four different classes a1a2a3a4a1a2a3a4 00000000 Singular 0 010 01 10 Non-singular 10 00 11 110 Uniqely decodable 0 10 110 111 Instantaneous Uniqely decodable Instantaneous Decoding probem: 010 could mean a 1 a 4 or a 2 or a 3 a 1. Decoding probem: 1100000000000000001… is uniqely decodable, but the first symbol ( a 3 or a 4 ) cannot be decoded until the third ’1’ arrives (Compare 11010 and 110010).

Data Compression  Efficient codes utilize the following properties: –Uneven symbol probability –Inter-symbol dependence (memory source) –Acceptable distortion  Examples: –The FIVO example –”There’s always an a 3 after a 1 ”. –Don’t care whether it’s a 3 or a 4.

 Consider, again, our favourite example code {a 1, …, a 4 } = {0, 10, 110, 111}.  The codewords are the leaves in a code tree.  Tree codes are comma-free and instantaneous. No codeword is a prefix of another! Part 2: Code Trees and Tree Codes 0 1 a1a1 0 1 a2a2 0 1 a3a3 a4a4

Kraft’s Inequality  For a uniqely decodable code with codeword lengths l i we have  Conversely, if this is valid for a given set of codeword lengths, it is possible to construct a code tree. (Proof: Sayood 2.4)

Kraft’s Inequality and Tree Codes  If KI is valid for a set of codeword lengths, there is a tree code with such lengths.  Proof: Create a maximal tree with the size from the longest codeword length l max. –The tree then has 2 l max leaves. –Place the codewords, cut the tree, and use KI to prove that there is enough leaves. –Let’s illustrate.

l1l1 Cannot be used: cut! Place l 1 in the tree. Then 2 l max – l 1 leaves disappear.....and 2 l max – 2 l max – l 1 = 2 l max (1 – 2 -l 1 ) leaves remain. Place l 2 in the tree. Then 2 l max (1 – 2 -l 1 – 2 -l 2 ) leaves remain. l2l2 l3l3 l4l4 After placing N codeword lengths, 2 l max (1 –  2 -l 1 ) leaves remain. Possible whenever KI is valid, i.e.,  2 -l 1 · 1. Try with {l i } = {1, 2, 3, 3} and {l i } = {1, 2, 2, 3} ! l max = 3 leads to this tree:

Optimal codeword lengths ) the entropy limit is reached! Part 3: Optimal Codes But what about the integer constraints? l i = – log p i is not always an integer! Average codeword length [bits/codeword] Kraft’s Inequality

The Source Coding Theorem  Assume that the source X is memory-free, and create the tree code for the extended source, i.e., blocks of n symbols.  We have:  We can come arbitrarily close to the entropy!

In Practice  Two practical problems need to be solved: –Bit-assignment –The integer contraint  Theoretically: Chose l i = – log p i –Rounding up not always the best! –Example: Binary source p 1 = 0.25, p 2 = 0.75 ) l 1 = log 4 = 2 l 2 = d – log 0.75 e = 1  Instead, use, e.g., the Huffman algorithm (D.Huffman, 1952) to create an optimal tree code!

Summary  Coding: Assigning binary codewords to (blocks of) source symbols.  Variable-length codes (VLC) and fixed-length codes.  Instantaneous codes ½ Uniqely decodable codes ½ Non-singular codes ½ All codes  Tree codes are instantaneous.  Tree code, Kraft’s Inequality.  The Source Coding Theorem.

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