EKT 357 Digital Communications. Properties of coding Basic coding algorithm Data compression Lossless Compression Lossy Compression.

Chapter 2 Source Coding (part 2)

EKT 357 Digital Communications

Chapter 2 (Part 2) Overview

Properties of coding Basic coding algorithm Data compression Lossless Compression Lossy Compression

Digital Communication System

Properties of coding

Code Types Fixed-length codes – all codewords have

the same length (number of bits)▪ A-000, B-001, C-010, D-011, E-100, F-101

Variable-length codes- may give different lengths to codewords▪ A-0, B-00, C-110, D-111, E-1000, F-1011

Uniquely Decodable Codes

Allow to invert the mapping to the original symbol alphabet.

A variable length code assigns a bit string (codeword) of variable length to every message value

e.g. a = 1, b = 01, c = 101, d = 011What if you get the sequence of bits1011 ?

Is it aba, ca, or, ad? A uniquely decodable code is a variable

length code in which bit strings can always be uniquely decomposed into its codewords.

Prefix-Free Property

No codeword be the prefix of any other code word. e.g a = 0, b = 110, c = 111, d = 10

A prefix code is a type of code system (typically a variable-length code) distinguished by its possession of the "prefix property", which requires that there is no code word in the system that is a prefix (initial segment) of any other code word in the system.

Basic coding algorithm Code word lengths are no longer fixed like ASCII. ASCII uses 8-bit patterns or bytes to identify which

letter is being represented.

Not all characters occur with the same frequency. Yet all characters are allocated the same amount of

space 1 char = 1 byte

Data Compression

For a binary file of length 1,000,000 bits contains 100,000 “1”s. This file can be compressed by more than a factor of 2 with the given of p=0.9 . Try to verify this using Source Entropy.

Data Compression

Data Compression

Data compression ratio is defined as the ratio between the uncompressed size and compressed size

Data Compression Methods

Data compression is about storing and sending a smaller number of bits.

There’re two major categories for methods to compress data: lossless and lossy methods

Data compression Encoding information in a relatively

smaller size than their original size▪ Like ZIP files (WinZIP), RAR files (WinRAR),TAR files etc..

Data compression: Lossless: the compressed data are

an exact copy of the original data Lossy: the compressed data may be

different than the original data

Data Compression

Lossless Compression Methods

In lossless methods, original data and the data after compression and decompression are exactly the same.

Redundant data is removed in compression and added during decompression.

Lossless methods are used when we can’t afford to lose any data: legal and medical documents, computer programs.

Lossless compression

In lossless data compression, the integrity of the data is preserved.

The original data and the data after compression and decompression are exactly the same because the compression and decompression algorithms are exactly the inverse of each other.

Example: Run-length coding Lempel-Ziv (L Z) coding (dictionary-based

encoding) Huffman coding

Run-length coding

Simplest method of compression. How: replace consecutive repeating

occurrences of a symbol by 1 occurrence of the symbol itself, then followed by the number of occurrences.

Run-length coding

The method can be more efficient if the data uses only 2 symbols (0s and 1s) in bit patterns and 1 symbol is more frequent than another.

Compression technique Represents data using value and run length Run length defined as number of consecutive

equal values

Introduction - Applications Useful for compressing data that contains

repeated values e.g. output from a filter, many consecutive

same values. Very simple compared with other

compression techniques

Example 1

A scan line of a binary digit is 00000 00000 00000 00000 00010 00000 00000 01000 00000 00000

Example 2

What does code X5 A9 represent using run-length encoding?

Run-length coding

Every code word is made up of a pair (g, l) where g is the gray level, and l is the number of pixels with that gray level (length, or “run”).

E.g.,56 56 56 82 82 82 83 80 56 56 56 56 56 80 80 80

creates the run-length code (56, 3)(82, 3)(83, 1)(80, 4)(56, 5).

The code is calculated row by row. Very efficient coding for binary data. Used in most fax machines and Image Coding

Run-length coding

Row 1

Row 2

Row 3

Row 4

Row 5

Row 6

Row 7

Row 8

8 8

8

Run-length coding

Row Run-Length Code1 (0,8)2 (0,2) (1,2) (2,1) (3,3)3 (0,1) (1,2) (3,3) (4,2)4 (0,1) (1,1) (3,2) (5,2) (4,2)5 (0,1) (2,1) (3,2) (5,3) (4,1)6 (0,2) (2,1) (3,2) (4,1) (8,2)7 (0,3) (2,2) (3,1) (4,2)8 (0,8)

Run-length coding

Compression Achieved

Original image requires 3 bits per pixel (in total - 8x8x4=256 bits).

Compressed image has 29 runs and needs 3+4=7 bits per

run (in total - 203 bits or 3.17 bits per pixel).

Row Run-Length Code

1 (0,8)

2 (0,2) (1,2) (2,1) (3,3)

3 (0,1) (1,2) (3,3) (4,2)

4 (0,1) (1,1) (3,2) (5,2) (4,2)

5 (0,1) (2,1) (3,2) (5,3) (4,1)

6 (0,2) (2,1) (3,2) (4,1) (8,2)

7 (0,3) (2,2) (3,1) (4,2)

8 (0,8)

Lempel-Ziv coding

It is dictionary-based encoding LZ creates its own dictionary (string of

bits), and replaces future occurrences of these strings by a shorter position string:

Basic idea: Create a dictionary(a table) of strings used during

communication.

If both sender and receiver have a copy of the dictionary, then previously-encountered strings can be substituted by their index in the dictionary.

Lempel-Ziv coding

Have 2 phases: Building an indexed dictionary Compressing a string of symbols

• Algorithm: Extract the smallest substring that cannot be

found in the remaining uncompressed string. Store that substring in the dictionary as a new

entry and assign it an index value. Substring is replaced with the index found in the

dictionary. Insert the index and the last character of the

substring into the compressed string.

Lempel-Ziv coding

Consists of scattered repetition bits or characters (strings)

E.g. A B B C B C A B A B C A A B C A A B

Lempel-Ziv coding

Original Code: ABBCBCABABCAABCAAB

The compressed message is: (0,A)(0,B)(0,C)(1,B)(2,C)(5,A)(2,A)(6,A)(8,B)

Lempel-Ziv coding

Example: Uncompressed String: ABBCBCABABCAABCAAB Number of bits = Total number of characters * 8

= 18 * 8 = 144 bits

Suppose the codewords are indexed starting from 1: Compressed string( codewords): (0,A)(0,B)(0,C)(1,B)(2,C)(5,A)(2,A)(6,A)

(8,B) Codeword index 1 2 3 4 5 6 7 8 9

Note: The above is just a representation, the commas and parentheses are not transmitted;

• Each code word consists of an integer and a character:

• The character is represented by 8 bits.

Lempel-Ziv coding

Codeword (0,A) (0,B) (0,C) (1,B) (2,C) (5,A) (2,A) (6,A) (8,B)index 1 2 3 4 5 6 7 8 9 Bits: (1 + 8) + (1 + 8) + (1 + 8) + (1 + 8) + (2 + 8) + (3 + 8) + (2 + 8) + (3+8) + (3+8) = 89 bits

The actual compressed message is: 0A 0B 0C 1B 10C 100A 10A 101A 111B

where each character is replaced by its binary 8-bit ASCII code.

Example: 3

Encode RSRTTUUTTRRTRSRRSSUU using Lempel-Ziv method.

Huffman coding

Huffman coding is a form of statistical coding

Huffman coding is a prefix-free, variable-length code that can be achieve shortest average code length.

Code word lengths vary and will be shorter for the more frequently used characters.

Background of Huffman of coding

Proposed by Dr. David A. Huffman in 1952 “A Method for the Construction of

Minimum Redundancy Codes”

Applicable to many forms of data transmission example: text files

Creating Huffman coding

1. Scan text to be compressed and tally occurrence of all characters.

2. Sort or prioritize characters based on number of occurrences in text.

3. Build Huffman code tree based on prioritized list.

4. Perform a traversal of tree to determine all code words.

5. Scan text again and create new file using the Huffman codes.

Huffman Coding (by example) A digital source generates five symbols

with the following probabilities:

S , P(s)=0.27 T, P(t)=0.25 U, P(u)=0.22 V,P(v)=0.17 W,P(w)=0.09

Use Huffman Coding algorithm to compress this source

Step 1: Arrange the symbols in a descending order according to their probabilities

S0.27

T0.25

U0.22

V0.17

W0.09

Huffman Coding (by example)

Step 2: take the symbols with the lowest probabilities and form a leaf

V0.17

U0.22

LIST

T0.25

S0.27

W0.09

V,W(x1)0.26

Huffman Coding (by example)

Step 3: Insert the parent node to the list

V0.17

U0.22

LIST

T0.25

S0.27

W0.09

V,W(x1)0.26

Huffman Coding (by example)

Step 3: Insert the parent node to the list

V0.17

U0.22

LIST

T0.25

S0.27

W0.09

V,W(x1)0.26

X10.26

Huffman Coding (by example)

Step 4: Repeat the same procedure on the updated list till we have only one node

U0.22

LIST

T0.25

S0.27

V0.17

W0.09

V,W(x1)0.26

X10.26

T0.25

U0.22

X20.47

Huffman Coding (by example)

LIST

S0.27

V0.17

W0.09

S0.27 X1

0.26

T0.25

U0.22

X20.47

X20.47

X10.26

X30.53

Huffman Coding (by example)

LIST

X30.53

X20.47

V0.17

W0.09

T0.25

S0.27

X10.26

X30.53

U0.22

X20.47

X41

Huffman Coding (by example)

V0.17

W0.09

T0.25

S0.27

X10.26

X30.53

U0.22

X20.47

X41

Step 5: Label each branch of the tree with “0” and “1”

Huffman Code Tree

0

0

0

0

1

11

1

Huffman Coding (by example)

V0.17

W0.09

T0.25

S0.27

X10.26

X30.53

U0.22

X20.47

X41

Huffman Code Tree

0

0

0

0

1

11

1

Huffman Coding (by example)

Codeword of w = 100

V0.17

W0.09

T0.25

T0.27

X10.26

X30.53

U0.22

X20.47

X41

Huffman Code Tree

0

0

0

0

1

11

1

Huffman Coding (by example)

Codeword of u=00

As a result:

Symbol Probability Codeword

S 0.27 11

T 0.25 01

U 0.22 00

V 0.17 101

W 0.09 100

Symbols with higher probability of occurrence have a shorter codeword length, while symbols with lower probability of occurrence have longer codeword length.

Average codeword length

The average codeword length achieved can be calculated by:

ni = Sum of the binary code lengths P(Xi) = Probability of that code

For the previous example we have the average codeword length as follows:

m

iii nXPL

1

)(

L (0.27 2) (0.25 2) (0.22 2) (0.17 3) (0.09 3)

L 2.26 bits

The Importance of Huffman Coding Algorithm

As seen by the previous example, the average codeword length calculated was 2.26 bits

Five different symbols “S,T,U,V,W” Without coding, we need three bits to represent all

of the symbols By using Huffman coding, we’ve reduced the

amount of bits to 2.26 bits Imagine transmitting 1000 symbols

▪ Without coding, we need 3000 bits to represent them

▪ With coding, we need only 2260 That is almost 25% reduction “25% compression”

Summary of Huffman Coding Huffman coding is a technique used to

compress files for transmission

Uses statistical coding more frequently used symbols have shorter

code words

Works well for text and fax transmissions

An application that uses several data structures

Example 3:

Building a tree by assuming that the relative frequencies are: A: 40 B: 20 C: 10 D: 10 R: 20

Lossy Compression Methods Used for compressing images and video

files (our eyes cannot distinguish subtle changes, so lossy data is acceptable).

Several methods:

JPEG: compress pictures and graphics MPEG: compress video MP3: compress audio

JPEG Compression: Basics Human vision is insensitive to high spatial

frequencies JPEG Takes advantage of this by compressing

high frequencies more coarsely and storing image as frequency data

JPEG is a “lossy” compression scheme.

Losslessly compressed image, ~150KB JPEG compressed, ~14KB

Baseline JPEG compression

Baseline JPEG compression

YCbCb colour space is based on YUV colour space

YUV signals are created from an original RGB (red, green and blue) source. The weighted values of R, G and B are added together to produce a single Y (lumsignal, representing the overall brightness, or luminance and chrominance (Cr, Cb) of that spot.

Y = luminanceCr, Cb = chrominance

Discrete cosine transform

DCT transforms the image from the spatial domain into the frequency domain

Next, each component (Y, Cb, Cr) of the image is "tiled" into sections of eight by eight pixels each, then each tile is converted to frequency space using a two-dimensional forward discrete cosine transform (DCT, type II). The 64 DCT basis functions

QuantizationThis is the main lossy operation in the whole process.

After the DCT has been performed on the 8x8 image block, the results are quantized in order to achieve large gains in compression ratio. Quantization refers to the process of representing the actual coefficient values as one of a set of predetermined allowable values, so that the overall data can be encoded in fewer bits (because the allowable values are a small fraction of all possible values).

Example of a quantizing matrix

The aim is to greatly reduce the amount of information in the high frequency components.

Example of Frequency Quantization with 8x8 blocks

-80

4 -6 6 2 -2 -2 0

24 -8 8 12 0 0 0 2

10 -4 0 -12 -4 4 4 -2

8 0 -2 -6 10 4 -2 0

18 4 -4 6 -8 -4 0 0

-2 8 6 -4 0 -2 0 0

12 0 6 0 0 0 -2 -2

0 8 0 -4 -2 0 0 0

16 11 10 16 24 40 51 61

12 12 14 19 26 58 60 55

14 13 16 24 40 57 69 56

14 17 22 29 51 87 80 62

18 22 37 56 68 109

103

77

24 35 55 64 81 104

113

92

49 64 78 87 103

121

120

101

72 92 95 98 112

100

103

99

-5 0 0 0 0 0 0 0

2 -1 1 1 0 0 0 0

1 0 0 -1 0 0 0 0

1 0 0 0 0 0 0 0

1 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0

Quantization Matrix to divide by

Quantized frequency values

Color space values (data)

Scanning and Compressing

-5 0 0 0 0 0 0 0

2 -1 1 1 0 0 0 0

1 0 0 -1 0 0 0 0

1 0 0 0 0 0 0 0

1 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0

Spatial Frequencies scanned in zig-zag pattern (note high frequencies mostly zero)

Run-Length Coding/ Huffman Coding used to losslessly record values in table

-5,0,2,1,-1,0,0,1,0,1,1,0,0,1,0,0,0,-1,0,0,… 0

Can be stored as:

(1,2),(0,1),(0,-1),(2,1),(1,1),(0,1),(2,1),(3,-1),EOB

So now we can all grow beards!

http://www.imaging.org/resources/jpegtutorial/jpgimag1.cfm

Quality factor =20