Data compression introduction

DATA COMPRESSION
Rahul V. Khanwani
Roll No. 47
Department Of Computer Science

Introduction
• WinRaR
• Now A days data And Information Being A
Major thing.
• The Data Compression Refers To the name
Compress. It Means To compress The data And
Utilize the System Space.
Rahul Khanvani For More Visit Binarybuzz.wordpress.com

Why To Utilize Space ?
• For Example
• Similar Kind Of Starting Character In Database
– Amit.
– Amin.
• Reducing Size Length
• Thus To Reduce Unnecessary Space We Need
Data Compression.
A M I T
R A H U L
Rahul Khanvani For
More Visit Binarybuzz.wordpress.com

Need Of Data Compression
• To Reduce The Space:
– Compression of space Depends on Compression
Technique
• Increase Channel bandwith:
– Send-Receive Data In Minimal Form
– Smaller Data Increase The Channel Bandwith
• Security:
– Compression Change The Original Value Of data.
Rahul Khanvani For

Types Of Data Compression
1. Lossless Compression
1. Shannon-Fano
2. Huffman
3. Lempel-Ziv (LZ)
4. Arithmetic Coding
5. Run Length Encoding
6. Burrows-Wheeler (BWT)
7. Deflate
2. Loosy Compression
1. Image
2. Audio
3. VideoRahul Khanvani For

Loosy data compression
• In this type of compression data which
was compressed are not recovered
properly.
• In this technique some part of data in
range of time period is drop in short
some part are cut from chain of data
bits.
Rahul Khanvani For

Lossless data compression
• In this compression technique
after compression at recovery
time x:-we will get data as we
have before compression.
– Ex:-
» Zip file
Rahul Khanvani For

Terms Of Compression
• Coding
– Describes the procedure defining the
transformation of symbols from one set
of symbols to another one.
• Encoding
– Process denotes the coding into a
particular destination format.
– Converting Bitmap to JPEG
• Decoding
– Process denotes the reverse process
related to Encoding
– JPEG to Bitmap
Rahul Khanvani For

Data compression an example
• Image Conversations:
• RAW
• BMP(bitmap image):
2.25MB
• TTIF(tagged image file
format):1.65MB
• PNG(Portable Network
Graphics):1.44MB
• GIF(Graphic Interchange
Format):254KB
• JPEG(Joint Photographic
Experts Group):291KB
Rahul Khanvani For

DATA
COMPRESSION
TECHNIQUES

Shannon-Fano
Huffman
Lempel-Ziv (LZ)
Arithmetic Coding
Run Length Encoding
Burrows-Wheeler (BWT)
Deflate
1
2
3
4
5
6
7 Rahul Khanvani For

SHANNON-FANO
• Developed In 1960.
• Shannon–Fano coding, named after Claude
Elwood Shannon and Robert Fano, is a
technique for constructing a prefix code
based on a set of symbols and their
probabilities.
• Also Known As Variable Length Coding (VLC).
• Top Down Approach.Rahul Khanvani For More Visit Binarybuzz.wordpress.com

Shannon-Fano Algorithm
1. For a given list of symbols, develop a corresponding list of
probabilities or frequency counts.
2. Sort the lists of symbols according to frequency, with the
most frequently occurring symbols at the left and the least
common at the right.
3. Divide the list into two parts, with the total frequency counts
of the left part being as close to the total of the right as
possible.
4. The left part of the list is assigned the binary digit 0, and the
right part is assigned the digit 1. This means that the codes
for the symbols in the first part will all start with 0, and the
codes in the second part will all start with 1.
5. Recursively apply the steps 3 and 4 to each of the two halves,
subdividing groups and adding bits to the codes until each
symbol has become a corresponding code leaf on the tree.Rahul Khanvani For More Visit Binarybuzz.wordpress.com

Example:
Symbol Count
A 15
B 7
C 6
D 6
E 5
Rahul Khanvani For

Example:
Symbol Count Value
A 15 0
B 7 0
C 6 1
D 6 1
E 5 1
22
17
Rahul Khanvani For

Example:
Symbol Count Value
A 15 00
C 6 1
D 6 1
E 5 1
B 7 01
Rahul Khanvani For

Example:
Symbol Count Value
A 15 00
C 6 10
B 7 01
D 6 110
E 5 111
Rahul Khanvani For

Example:
Symbol Count Value
A 15 00
C 6 10
B 7 01
D 6 11
E 5 11
Rahul Khanvani For

Example:
Symbol Count Value
A 15 00
C 6 10
B 7 01
D 6 110
E 5 110
39
Rahul Khanvani For

Conclusion
• Shannon–Fano is almost never used.
• Huffmam coding is almost as computationally
simple and produces prefix codes that always
achieve the lowest expected code word length.
• Shannon–Fano coding is used in the IMPLODE
compression method, which is part of the ZIP
file format, where it is desired to apply a simple
algorithm with high performance and minimum
requirements for programming.

THANK YOU

Data compression introduction

Recommended

Recommended

More Related Content

What's hot

What's hot (20)

Viewers also liked

Viewers also liked (19)

Similar to Data compression introduction

Similar to Data compression introduction (20)

More from Rahul Khanwani

More from Rahul Khanwani (7)

Recently uploaded

Recently uploaded (20)

Data compression introduction