Image compression using adaptive visual vector quantization

Pao Yue-kong Library Electronic Theses Database

Image compression using adaptive visual vector quantization

 

Author: Mak, Siu-fai
Title: Image compression using adaptive visual vector quantization
Degree: M.Sc.
Year: 1997
Subject: Image processing -- Data processing
Vector processing (Computer science)
Hong Kong Polytechnic University -- Dissertations
Department: Multi-disciplinary Studies
Pages: 73 leaves : ill. ; 30 cm
Language: English
InnoPac Record: http://library.polyu.edu.hk/record=b1403347
URI: http://theses.lib.polyu.edu.hk/handle/200/1847
Abstract: The aim of this study is to fully evaluate the performance of the existing Visual Vector Quantization (VVQ) compression technique and to improve this technique by developing new codebook generation methods. In particular, fuzzy membership functions are applied to generate image-adaptive codebooks. Chapter 1 presents an overview of the image compression methods, and the objectives of this project. Background information is included to explain a source of motivation and inspiration behind this project. Chapter 2 gives a detailed description of what I have studied. These include vector quantization, visual vector quantization, K-means clustering method, back-propagation neural networks and fuzzy vector quantization. Chapter 3 presents a number of simulation results in the form of graphs and figures, each of which is followed by a brief discussion. Based on these results, various implementation are implemented to enhance the performance of compression. Chapter 4 is the conclusion of this dissertation. Further development is proposed in chapter 5.

Files in this item

Files Size Format
b14033471.pdf 2.907Mb PDF
Copyright Undertaking
As a bona fide Library user, I declare that:
  1. I will abide by the rules and legal ordinances governing copyright regarding the use of the Database.
  2. I will use the Database for the purpose of my research or private study only and not for circulation or further reproduction or any other purpose.
  3. I agree to indemnify and hold the University harmless from and against any loss, damage, cost, liability or expenses arising from copyright infringement or unauthorized usage.
By downloading any item(s) listed above, you acknowledge that you have read and understood the copyright undertaking as stated above, and agree to be bound by all of its terms.

     

Quick Search

Browse

More Information