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Next: MC397 Programming Secure and Distributed Systems
Up: Year 3
Previous: MC395 Software Measurement and Quality Assurance
MC396 Compression Methods for Multimedia
Credits: 20 |
Convenor: Prof. R. Raman |
Semester: 2 |
Prerequisites: |
essential:
MC111, MC116, MC117
|
desirable:
|
Assessment: |
Coursework: 30% |
Three hour exam in January: 70% |
Lectures: |
36 |
Problem Classes: |
6 |
Tutorials: |
none |
Private Study: |
96 |
Labs: |
none |
Seminars: |
none |
Project: |
none |
Other: |
none |
Surgeries: |
12 |
Total: |
150 |
Explanation of Pre-requisites
There are two main prerequisites. Firstly, students should have
some knowledge of how data of various kinds (numbers, characters,
images and sound) are represented digitally in uncompressed format.
This will be reviewed rapidly at the start of the course.
Some elementary mathematics is also required.
In particular, students should have seen the basic
trigonometric functions--
,
and should know how
to measure angles in radians; they should have had some
exposure to probability, in particular the notion
of the expected value of a variable; and also
they should know about transposition and multiplication of matrices.
Basic familiarity with the elements of computer systems and networks
is also desirable.
Course Description
Data compression is about finding novel ways of representing data
so that it takes very little storage, with the proviso that it should
be possible to reconstruct the original data from the compressed
version. Compression is essential when storage space is at
a premium or when data needs to be transmitted and bandwidth
is at a premium (which is almost always). The first thing
that one learns about compression is that there is no ``one size fits all''
approach: the essence of compression is to determine characteristics
of the data that one is trying to compress, from which we deduce
patterns that one can exploit to get a compact representation.
This gives rise to a variety of data modelling and representation
techniques, which is at the heart of compression.
The convergence of the communications, computing and entertainment
industries has made data compression a part of everyday life (e.g. MP3,
DVD and Digital TV) and has thrown up a number of exciting new
opportunities for new applications of compression technologies.
Aims
To study methods for compression of symbolic data as well
as audio, image and video data. To gain an appreciation of
the ubiquity and importance of compression technologies.
Objectives
- Students will have broad knowledge of compression techniques
as well as the mathematical foundations of data compression.
- Factual knowledge about existing compression standards or
commonly-used compression utilities.
- An understanding of the ubiquity and importance of
compression technologies in today's environment.
- An elementary understanding of the need for
modelling data and the underlying issues.
Transferable Skills
The ability to combine factual knowledge with problem solving skills
will pervade the course, and will benefit students within academia and
in real life computing situations.
Syllabus
Introduction:
Raw multimedia data representation,
Transmission medium characteristics,
Data compression,
Adaptive and non-adaptive methods,
Lossy and lossless compression,
Introduction to information theory and
Theoretical limits of compressibility.
Compressing symbolic data:
Run-length coding,
Entropy coders: Huffman coding, arithmetic coding,
Dictionary coders: LZ77, LZW,
Other text compression methods: Block-sorting.
Standard text compression utilities: compress, zip.
Image compression:
Monochrome, facsimile and grayscale compression,
GIF compression,
JPEG compression,
Video compression:
Frame-by-frame compression: M-JPEG.
Inter-frame compression: MPEG.
Audio compression:
Speech coding: ADPCM;
CD-quality audio: MPEG layer 3.
Audio formats: WAV, VOC, SND and MIDI.
Compression applications:
Computer system applications,
Communication network applications,
Broadcast media applications,
Consumer electronics applications,
Publishing applications,
Entertainment applications,
Healthcare applications.
Managing compressed data:
Self-identifying compressed data,
Error-proofing compression algorithms,
Interaction between compression and other functions,
Interaction between compression algorithms,
Operating on compressed data,
Archiving compressed data.
Reading list
Recommended:
Khalid Sayood,
Introduction to Data Compression, 2nd ed.,
Morgan Kaufmann Publishers, 2000..
Roy Hoffman,
Data compression in digital systems.,
Chapman and Hall Digital Multimedia Standards Series, 1997..
Background:
Andrew S. Tanenbaum,
Structured Computer Organization, 4th Edition,
Prentice Hall, 1999..
,
The comp.compression FAQ.,
www.faqs.org/faqs/compression-faq/.
Next: MC397 Programming Secure and Distributed Systems
Up: Year 3
Previous: MC395 Software Measurement and Quality Assurance
Author: S. J. Ambler, tel: +44 (0)116 252 3884
Last updated: 2001-09-20
MCS Web Maintainer
This document has been approved by the Head of Department.
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