Compression technologies

1
Under The Guidance of :- prof. Sidramayya S.M.
Presented by
Anil Khandekar
2BU14EC403
Electronics And Communication Engineering
SGBIT, Belgaum

CONTENTS
 Introduction
 Motivation
 Literature survey
 data compression methods
 lossless compression
• Run-length encoding
• Huffman coding
 Lossy compression methods
• Jpeg process
• Mpeg process
 Applications
 Conclusion
 References
2

INTRODUCTION
 Data compression is often referred to as coding, where coding
is a very general term encompassing any special representation
of data which satisfies a given need.
 Information theory is defined to be the study of efficient coding
and its consequences, in the form of speed of transmission and
probability of error.
 Data compression may be viewed as a branch of information
theory in which the primary objective is to minimize the
amount of data to be transmitted.
3

Motivation
 To broaden knowledge of compression techniques as well as
the mathematical foundations of data compression.
 To become aware of existing compression standards and some
compression utilities available.
 We can improve our programming skills by doing the
laboratory work on Data Compression.
4

Literature survey
 IEEE Explore Lossless data compression techniques, Published
in WESCON/94. Conference Record
Which are the different techniques of lossless data compression is explained
in detail.
 Introduction To Data Compression, 3rd Edition Paperback – 2010
by Sayood Khalid
Lossy compression technique and their different types are explained
 Information Theory and Coding by Giridhar , Pooja publication , 2014
edition
We get to know about different encoding algorithm such as Huffman coding
technique etc .
5

Data compression methods
 Data compression means sending or storing a smaller number
of bits.
6

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 encoding
 Huffman encoding
7

Run-length Encoding
 It does not need knowledge of the frequently of occurrence of
symbols and can be very efficient if data are represented as 0s
and 1s.
 For example:
8

Huffman coding
 In Huffman coding, you assign shorter codes to symbols that
occur more frequently and longer codes to those that occur
less frequently.
 For example:
9
Character A B C D E
------------------------------------------------------
Frequency 17 12 12 27 32

Lossy compression methods
 Loss of information is acceptable in a picture of video.
 The reason is that our eyes and ears cannot distinguish subtle
changes.
 Loss of information is not acceptable in a text file or a program
file.
 For examples:
 Joint photographic experts group (JPEG)
 Motion picture experts group (MPEG)
11

JPEG process
 DTC: discrete cosine transform
 Quantization
 Compression
12

Video compression--MPEG
 MPEG method
 Spatial compression
 The spatial compression of each frame is done with JPEG.
 Temporal compression
 The temporal compression removes the redundant frames.
 MPEG method first divides frames into three categories:
I-frames, P-frames, B-frames.
13

MPEG frames
 I-frames: (intra-coded frame)
 It is an independent frame that is not related to any other frame.
 They are present at regular intervals.
 I-frames are independent of other frames and cannot be constructed from
other frames.
 P-frames: (predicted frame)
 It is related to the preceding I-frame or P-frame.
 Each P-frame contains only the changes from the preceding frame.
 P-frames can be constructed only from previous I- or P-frames.
 B-frames: (bidirectional frame)
 It is relative to the preceding and following I-frame or P-frame.
 Each B-frame is relative to the past and the future.
 A B-frame is never related to another B-frame.
14

Applications
 satellite imagery
 mini discs
 MP3 technology
 fax
 digital cameras
 DVD technology
 Modems
 wireless telephony
 database design
 storage and transmission of CT and MRI scans
 Mammography
 digital images, high definition television (HDTV), and video games
15

Conclusion And Future Scope
 Conclusion
Image coding based on models of human perception, scalability,
robustness, error resilience, and complexity are a few of the many
challenges in image coding to be fully resolved and may affect
image data compression performance in the years to come.
 Future Scope
Data compression that make use of the archive data format
for maintaining high security within the system using the
encryption of the data packets. data compression increases
the communication channel capacity.
16

References
 International Journal of Advanced Research in Computer Science
and Software Engineering, Volume 3, Issue 10, October 2013
'Multimedia Data Compression Techniques‘
 Sachin Dhawan-’A review of image compression and comparison of
its algorithms, IJECT Vol. 2, Issue 1’
 IEEE Xplore Lossless data compression techniques, Published
in WESCON/94. Idea/Microelectronics. Conference Record
 Wallace, G., The JPEG still picture compression standard,
Communications of the ACM, 34 (199 ) 31-44.
17

Compression technologies

More Related Content

What's hot (20)

Similar to Compression technologies (20)

Recently uploaded (20)

Compression technologies