DEVELOPMENT OF AN IMPROVED APPROACH TO BIOMETRIC FINGERPRINT IMAGE COMPRESSION USING COIFLET SIGNAL TRANSFORMATION ALGORITHM


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TABLE OF CONTENTS

Title page
Abstract
Table of Contents
Abbreviations, Definitions, Glossary and Symbols

CHAPTER ONE: INTRODUCTION
1.1       Background of Study
1.2       Types of Digital Image
1.2.1    Binary Image
1.2.2    Grayscale Image
1.2.3    True Colour Image
1.2.4    Indexed Image
1.3       Data Redundancy
1.3.1    Coding Redundancy
1.3.2    Interpixel Redundancy
1.3.3    Psychovisual Redundancy
1.4       Characteristics of Fingerprint Image
1.5       Fingerprint Image Compression Techniques and Standards
1.6       Motivation
1.7       Aim and objectives
1.8       Statement of Problem
1.9       Justification of Study
1.10     Methodology
1.11     Significance of Study
1.12     Scope and Limitation of Work
1.13     Thesis Outline

CHAPTER TWO: LITERATURE REVIEW
2.1       Introduction
2.2       Review of Fundamental Concepts
2.2.1    Transformation
2.2.2    Quantization
2.2.3    Entropy Coding
2.2.4   Validation and Performance Measures
2.3       Review of Similar Works

CHAPTER THREE: MATERIALS AND METHODS
3.1       Introduction
3.2       Materials
3.3       Methods
3.3.1   2-D Discrete Wavelet Transform (DWT) of Source Fingerprint Image
3.3.2   Non-Uniform Scalar Quantization using Lloyd-Max Procedure
3.3.3   Arithmetic Entropy Coding to Generate Image Bit-Streams
3.3.4    Reconstruction of Source Fingerprint Image from Compressed Bit-Stream
3.3.5    Validation and Evaluation of the Performance of Compression Algorithm

CHAPTER FOUR: RESULTS AND DISCUSSIONS
4.1       Introduction
4.1       Results of Performance Analysis of Wavelet Bases
4.2       Results of the Quantization Schemes
4.3       Results of Performance Evaluation of the Compression Algorithm
4.4       Discussion of Results
4.5       Performance Analysis of Wavelet Bases
4.6       Comparative Analysis of Global Threshold and Level-Dependent Threshold Strategies
4.7       Comparison of the Non-uniform Quantization and Uniform Quantization Methods
4.8       Performance Evaluation of the Compression Algorithm
4.9       Contribution to Knowledge

CHAPTER FIVE: CONCLUSION AND RECOMMENDATIONS
5.1       Conclusion
5.2       Recommendations
References
Appendix



ABSTRACT


Biometric fingerprint images require substantial storage, transmission and computation costs, thus their compression is advantageous to reduce these requirements. This research work presents a novel approach to biometric fingerprint image compression by the innovative application of non-uniform quantization scheme in combination with level-dependent threshold strategy applied to wavelet transformation as opposed to the widely used uniform quantization scheme. Comparative analysis of Coiflet wavelets implemented with level dependent thresholds and Daubechies wavelets were conducted on the basis of percentage retained energy, RE (%). The RE (%) values for Coiflet wavelet ranged from 99.32% to 99.69% as opposed to the values for Daubechies wavelet which ranged from 98.45% to 99.15%. These results revealed that the Coiflet wavelet bases performed better than the Daubechies wavelet. Hence, the choice of Coiflet wavelet for image transformation in the proposed compression algorithm was justified. The performance analysis of uniform and non-uniform scalar quantization schemes for biometric fingerprint image compression was conducted. The non-uniform quantization method based on Lloyd-Max approach performed better than the uniform quantization method used in the existing fingerprint compression standards. The Signal-to-Quantization Noise Ratio (SQNR) values for non-uniform quantization increased from 19.2977 dB for 3 bit per pixel (bpp) to 44.6083 dB for 7 bpp whereas for the same range (3 bpp to 7 bpp) for uniform quantization, SQNR values increased from 17.0903 dB to 40.1349 dB. Therefore, non-uniform quantization based on Lloyd-Max approach was employed for this compression algorithm. The implementation of the proposed biometric fingerprint image compression algorithm involved three stages, namely: the transformation of biometric fingerprint image; non-uniform quantization of transformed image and the entropy coding which is the final stage. In order to determine the overall performance of the algorithm, Peak Signal-to-Noise Ratio (PSNR) and Compression Ratio (CR) were used as performance metrics. PSNR was used as a measure of the resultant image quality after compression and the Compression Ratio was used as a measure of the degree of compression achievable. A trade-off was made between the achievable compression ratio and the realizable image quality which is a function of the achievable PSNR in the overall compression process. The overall performance of the proposed compression algorithm achieved an improvement in terms of compression ratio of 20:1 over the existing compression standard for biometric applications which have a compression ratio limit of 15:1. The improvement was largely due to the novel approach employed in this research work as stated above.




CHAPTER ONE

INTRODUCTION



1.1              Background of Study

Images contain large amount of information that requires huge storage space and large transmission bandwidth. Image data processing and storage attract cost and the cost is directly proportional to the size of data. In spite of the advancements made in mass storage and processing capacities, these have continued to fall below capacity requirements of application systems (Ashok et al., 2010). Therefore, it is advantageous to compress an image by storing only the essential information needed to reconstruct the image. An image can be thought of as a matrix of pixel (or intensity) values and in order to compress it, redundancies must be exploited. Image compression is the general term for the various algorithms that have been developed to address these problems.

Data compression algorithms are categorized into two, namely; lossless and lossy compression techniques. A lossless technique guarantees that the compressed data is identical to the original data whereas in lossy compression technique, images are compressed with some degree of data loss or degradation while still retaining their essential features. This distinction is important because lossy techniques are much more effective at compression than lossless methods. Lossy technique is the preferred choice for fingerprint image compression to reduce computation, storage and transmission costs. Huge volumes of fingerprint images that need to be stored and transmitted over a network of biometric databases are an excellent example of why data compression is important. The cardinal goal of image compression is to obtain the best possible image quality at a reduced storage, transmission and computation costs (Mallat, 2009).....


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