Image Compression by Wavelet Transform.

Digital images are widely used in computer applications. Uncompressed digital images require considerable storage capacity and transmission bandwidth. Efficient image compression solutions are becoming more critical with the recent growth of data intensive, multimedia-based web applications. This thesis studies image compression with wavelet transforms. As a necessary background, the basic concepts of graphical image storage and currently used compression algorithms are discussed. The mathematical properties of several types of wavelets, including Haar, Daubechies, and biorthogonal spline wavelets are covered and the Enbedded Zerotree Wavelet (EZW) coding algorithm is introduced. The last part of the thesis analyzes the compression results to compare the wavelet types

Self-similarity and wavelet forms for the compression of still image and video data

This thesis is concerned with the methods used to reduce the data volume required to represent still images and video sequences. The number of disparate still image and video coding methods increases almost daily. Recently, two new strategies have emerged and have stimulated widespread research. These are the fractal method and the wavelet transform. In this thesis, it will be argued that the two methods share a common principle: that of self-similarity. The two will be related concretely via an image coding algorithm which combines the two, normally disparate, strategies. The wavelet transform is an orientation selective transform. It will be shown that the selectivity of the conventional transform is not sufficient to allow exploitation of self-similarity while keeping computational cost low. To address this, a new wavelet transform is presented which allows for greater orientation selectivity, while maintaining the orthogonality and data volume of the conventional wavelet transform. Many designs for vector quantizers have been published recently and another is added to the gamut by this work. The tree structured vector quantizer presented here is on-line and self structuring, requiring no distinct training phase. Combining these into a still image data compression system produces results which are among the best that have been published to date. An extension of the two dimensional wavelet transform to encompass the time dimension is straightforward and this work attempts to extrapolate some of its properties into three dimensions. The vector quantizer is then applied to three dimensional image data to produce a video coding system which, while not optimal, produces very encouraging results

Hardware Acceleration of the Embedded Zerotree Wavelet Algorithm

The goal of this project was to gain experience in designing and implementing a microelectronic system to acclerate the execution of a time-consuming software algorithm, the Embedded Zerotree Wavelet (EZW), which is used in multimedia applications. The algorithm was implemented using MATLAB to be certain it was fully understood and to serve as a validation reference. Then, the algorithm was mapped into a hardware description language, VHDL, and its resulting implementation verified with the golden reference. The hardware description was then targeted to a field-programmable gate array (FPGA). Significant acceleration was achieved since the hardware implementation in a FPGA (Xilinx Virtex-1000E using a 8.315 MHz clock) ran 10,000 times faster than the MATLAB implementation on a SUN-220 workstation. Additional speedup exploiting the parallel capabilities of the FPGA was not achieved since the EZW algorithm utilizes only sequential operations

Problem-based learning (PBL) awareness among academic staff in Universiti Tun Hussein Onn Malaysia (UTHM)

The present study was conducted to determine whether the academic staff in UTHM was aware of Problem-based Learning (PBL) as an instructional approach. It was significant to identify if the academic staff in Universiti Tun Hussein Onn Malaysia (UTHM) had the knowledge about PBL. It was also crucial to know if the academic staff was aware of PBL as a method of teaching their courses in class as this could give the feedback to the university on the use of PBL among academic staff and measures to be taken to help improve their teaching experience. A workshop could also be designed if the academic staff in UTHM was interested to know more about PBL and how it could be used in their classroom. The objective of this study was to identify the awareness of PBL among academic staff in UTHM. This study was conducted via a quantitative method using a questionnaire adapted from the Awareness Questionnaire (AQ). 100 respondents were involved in this study. The findings indicated that the awareness of PBL among UTHM academic staff was moderate. It is a hope that more exposure could be done as PBL is seen as a promising approach in the learning process. In conclusion, the academic staff in UTHM has a moderate level of knowledge about PBL as a teaching methodology

Image Compression Techniques by using Wavelet Transform

This paper is concerned with a certain type of compression techniques by using wavelet transforms. Wavelets are used to characterize a complex pattern as a series of simple patterns and coefficients that, when multiplied and summed, reproduce the original pattern.  The data compression schemes can be divided into lossless and lossy compression. Lossy compression generally provides much higher compression than lossless compression. Wavelets are a class of functions used to localize a given signal in both space and scaling domains. A MinImage was originally created to test one type of wavelet and the additional functionality was added to Image to support other wavelet types, and the EZW coding algorithm was implemented to achieve better compression. Keywords: Wavelet Transforms, Image Compression, Lossless Compression, Lossy Compressio

A review on region of interest-based hybrid medical image compression algorithms

Digital medical images have become a vital resource that supports decision-making and treatment procedures in healthcare facilities. The medical image consumes large sizes of memory, and the size keeps on growth due to the trend of medical image technology. The technology of telemedicine encourages the medical practitioner to share the medical image to support knowledge sharing to diagnose and analyse the image. The healthcare system needs to ensure distributes the medical image accurately with zero loss of information, fast and secure. Image compression is beneficial in ensuring that achieve the goal of sharing this data. The region of interest-based hybrid medical compression algorithm plays the parts to reduce the image size and shorten the time of medical image compression process. Various studies have enhanced by combining numerous techniques to get an ideal result. This paper reviews the previous works conducted on a region of interest-based hybrid medical image compression algorithms

Context-based compression algorithms for text and image data.

Wong Ling.Thesis (M.Phil.)--Chinese University of Hong Kong, 1997.Includes bibliographical references (leaves 80-85).ABSTRACT --- p.1Chapter 1. --- INTRODUCTION --- p.2Chapter 1.1 --- motivation --- p.4Chapter 1.2 --- Original Contributions --- p.5Chapter 1.3 --- thesis Structure --- p.5Chapter 2. --- BACKGROUND --- p.7Chapter 2.1 --- information theory --- p.7Chapter 2.2 --- early compression --- p.8Chapter 2.2.1 --- Some Source Codes --- p.10Chapter 2.2.1.1 --- Huffman Code --- p.10Chapter 2.2.1.2 --- Tutstall Code --- p.10Chapter 2.2.1.3 --- Arithmetic Code --- p.11Chapter 2.3 --- modern techniques for compression --- p.14Chapter 2.3.1 --- Statistical Modeling --- p.14Chapter 2.3.1.1 --- Context Modeling --- p.15Chapter 2.3.1.2 --- State Based Modeling --- p.17Chapter 2.3.2 --- Dictionary Based Compression --- p.17Chapter 2.3.2.1 --- LZ-compression --- p.19Chapter 2.3.3 --- Other Compression Techniques --- p.20Chapter 2.3.3.1 --- Block Sorting --- p.20Chapter 2.3.3.2 --- Context Tree Weighting --- p.21Chapter 3. --- SYMBOL REMAPPING --- p.22Chapter 3. 1 --- reviews on Block Sorting --- p.22Chapter 3.1.1 --- Forward Transformation --- p.23Chapter 3.1.2 --- Inverse Transformation --- p.24Chapter 3.2 --- Ordering Method --- p.25Chapter 3.3 --- discussions --- p.27Chapter 4. --- CONTENT PREDICTION --- p.29Chapter 4.1 --- Prediction and Ranking Schemes --- p.29Chapter 4.1.1 --- Content Predictor --- p.29Chapter 4.1.2 --- Ranking Techn ique --- p.30Chapter 4.2 --- Reviews on Context Sorting --- p.31Chapter 4.2.1 --- Context Sorting basis --- p.31Chapter 4.3 --- General Framework of Content Prediction --- p.31Chapter 4.3.1 --- A Baseline Version --- p.32Chapter 4.3.2 --- Context Length Merge --- p.34Chapter 4.4 --- Discussions --- p.36Chapter 5. --- BOUNDED-LENGTH BLOCK SORTING --- p.38Chapter 5.1 --- block sorting with bounded context length --- p.38Chapter 5.1.1 --- Forward Transformation --- p.38Chapter 5.1.2 --- Reverse Transformation --- p.39Chapter 5.2 --- Locally Adaptive Entropy Coding --- p.43Chapter 5.3 --- discussion --- p.45Chapter 6. --- CONTEXT CODING FOR IMAGE DATA --- p.47Chapter 6.1 --- Digital Images --- p.47Chapter 6.1.1 --- Redundancy --- p.48Chapter 6.2 --- model of a compression system --- p.49Chapter 6.2.1 --- Representation --- p.49Chapter 6.2.2 --- Quantization --- p.50Chapter 6.2.3 --- Lossless coding --- p.51Chapter 6.3 --- The Embedded Zerotree Wavelet Coding --- p.51Chapter 6.3.1 --- Simple Zerotree-like Implementation --- p.53Chapter 6.3.2 --- Analysis of Zerotree Coding --- p.54Chapter 6.3.2.1 --- Linkage between Coefficients --- p.55Chapter 6.3.2.2 --- Design of Uniform Threshold Quantizer with Dead Zone --- p.58Chapter 6.4 --- Extensions on Wavelet Coding --- p.59Chapter 6.4.1 --- Coefficients Scanning --- p.60Chapter 6.5 --- Discussions --- p.61Chapter 7. --- CONCLUSIONS --- p.63Chapter 7.1 --- Future Research --- p.64APPENDIX --- p.65Chapter A --- Lossless Compression Results --- p.65Chapter B --- Image Compression Standards --- p.72Chapter C --- human Visual System Characteristics --- p.75Chapter D --- Lossy Compression Results --- p.76COMPRESSION GALLERY --- p.77Context-based Wavelet Coding --- p.75RD-OPT-based jpeg Compression --- p.76SPIHT Wavelet Compression --- p.77REFERENCES --- p.8

Wavelets and Imaging Informatics: A Review of the Literature

AbstractModern medicine is a field that has been revolutionized by the emergence of computer and imaging technology. It is increasingly difficult, however, to manage the ever-growing enormous amount of medical imaging information available in digital formats. Numerous techniques have been developed to make the imaging information more easily accessible and to perform analysis automatically. Among these techniques, wavelet transforms have proven prominently useful not only for biomedical imaging but also for signal and image processing in general. Wavelet transforms decompose a signal into frequency bands, the width of which are determined by a dyadic scheme. This particular way of dividing frequency bands matches the statistical properties of most images very well. During the past decade, there has been active research in applying wavelets to various aspects of imaging informatics, including compression, enhancements, analysis, classification, and retrieval. This review represents a survey of the most significant practical and theoretical advances in the field of wavelet-based imaging informatics

Depth-first search embedded wavelet algorithm for hardware implementation

The emerging technology of image communication over wireless transmission channels requires several new challenges to be simultaneously met at the algorithm and architecture levels. At the algorithm level, desirable features include high coding performance, bit stream scalability, robustness to transmission errors and suitability for content-based coding schemes. At the architecture level, we require efficient architectures for construction of portable devices with small size and low power consumption. An important question is to ask if a single coding algorithm can be designed to meet the diverse requirements. Recently, researchers working on improving different features have converged on a set of coding schemes commonly known as embedded wavelet algorithms. Currently, these algorithms enjoy the highest coding performances reported in the literature. In addition, embedded wavelet algorithms have the natural feature of being able to meet a target bit rate precisely. Furthermore work on improving the algorithm robustness has shown much promise. The potential of embedded wavelet techniques has been acknowledged by its inclusion in the new JPEG2000 and MPEG-4 image and video coding standards