Universal Lossless Source Coding Techniques for Images and Short Data Sequences
(2001)- Abstract
- In this thesis various topics in universal lossless source coding are discussed and analyzed. The main focus in this work is on lossless data compression of grayscale still images. Such images are, for example, frequently occurring in medical imaging.
Based on theoretical considerations and empirical observations new compression algorithms are presented that are, in terms of compression performance, efficient compared to traditional methods.
This work includes research on how to use the Context Tree Weighting algorithm, linear prediction and probability assignment techniques in lossless data compression. The performance of these algorithms/methods is studied both asymptotically and for usage on short... (More) - In this thesis various topics in universal lossless source coding are discussed and analyzed. The main focus in this work is on lossless data compression of grayscale still images. Such images are, for example, frequently occurring in medical imaging.
Based on theoretical considerations and empirical observations new compression algorithms are presented that are, in terms of compression performance, efficient compared to traditional methods.
This work includes research on how to use the Context Tree Weighting algorithm, linear prediction and probability assignment techniques in lossless data compression. The performance of these algorithms/methods is studied both asymptotically and for usage on short data sequences.
The presented techniques can be used separately or together when designing efficient lossless data compression systems. (Less) - Abstract (Swedish)
- Popular Abstract in Swedish
Denna avhandling behandlar olika metoder för datakompression och speciellt metoder som är lämpliga för komprimering av digitala bilder. Avsikten med datakompressionen är att finna en representation av den ursprungliga datan (tex en bild) som upptar mindre plats på tex ett skivminne.
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/20364
- author
- Ekstrand, Nicklas LU
- supervisor
- opponent
-
- Willems, Frans
- organization
- publishing date
- 2001
- type
- Thesis
- publication status
- published
- subject
- keywords
- CMF, PPA, linear prediction, prediction, local optimization, JPEG, context tree weighting, lossless image compression, Source coding, universal source coding, ARQ, Electronics and Electrical technology, Elektronik och elektroteknik, Imaging, image processing, Bildbehandling
- pages
- 132 pages
- publisher
- Department of Information Technology, Lund Univeristy
- defense location
- E-building E:1406
- defense date
- 2001-04-06 10:15:00
- external identifiers
-
- other:ISRN: LUTEDX/TEIT-01/1017-SE
- ISBN
- 91-7167-020-3
- language
- English
- LU publication?
- no
- id
- d555989a-d3ac-436c-835b-91a6a4b1a736 (old id 20364)
- date added to LUP
- 2016-04-04 10:10:23
- date last changed
- 2018-11-21 20:57:13
@phdthesis{d555989a-d3ac-436c-835b-91a6a4b1a736,
abstract = {{In this thesis various topics in universal lossless source coding are discussed and analyzed. The main focus in this work is on lossless data compression of grayscale still images. Such images are, for example, frequently occurring in medical imaging.<br/><br>
<br/><br>
Based on theoretical considerations and empirical observations new compression algorithms are presented that are, in terms of compression performance, efficient compared to traditional methods.<br/><br>
<br/><br>
This work includes research on how to use the Context Tree Weighting algorithm, linear prediction and probability assignment techniques in lossless data compression. The performance of these algorithms/methods is studied both asymptotically and for usage on short data sequences.<br/><br>
<br/><br>
The presented techniques can be used separately or together when designing efficient lossless data compression systems.}},
author = {{Ekstrand, Nicklas}},
isbn = {{91-7167-020-3}},
keywords = {{CMF; PPA; linear prediction; prediction; local optimization; JPEG; context tree weighting; lossless image compression; Source coding; universal source coding; ARQ; Electronics and Electrical technology; Elektronik och elektroteknik; Imaging; image processing; Bildbehandling}},
language = {{eng}},
publisher = {{Department of Information Technology, Lund Univeristy}},
title = {{Universal Lossless Source Coding Techniques for Images and Short Data Sequences}},
year = {{2001}},
}