Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor | Multi-disciplinary Studies | en_US |
| dc.creator | Mak, Siu-fai | - |
| dc.identifier.uri | https://theses.lib.polyu.edu.hk/handle/200/1847 | - |
| dc.language | English | en_US |
| dc.publisher | Hong Kong Polytechnic University | - |
| dc.rights | All rights reserved | en_US |
| dc.title | Image compression using adaptive visual vector quantization | en_US |
| dcterms.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. | en_US |
| dcterms.extent | 73 leaves : ill. ; 30 cm | en_US |
| dcterms.isPartOf | PolyU Electronic Theses | en_US |
| dcterms.issued | 1997 | en_US |
| dcterms.educationalLevel | All Master | en_US |
| dcterms.educationalLevel | M.Sc. | en_US |
| dcterms.LCSH | Image processing -- Data processing | en_US |
| dcterms.LCSH | Vector processing (Computer science) | en_US |
| dcterms.LCSH | Hong Kong Polytechnic University -- Dissertations | en_US |
| dcterms.accessRights | restricted access | en_US |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| b14033471.pdf | For All Users (off-campus access for PolyU Staff & Students only) | 2.84 MB | Adobe PDF | View/Open |
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