Thesis Abstract

Abstract
Fractal geometry has emerged as a powerful tool for addressing complex patterns and structures in various fields. This thesis explores the applications of fractal geometry in image compression, aiming to enhance the efficiency and quality of image compression techniques. The research begins by providing an introduction to the topic, followed by a comprehensive background study on fractal geometry and its relevance to image processing. The problem statement highlights the challenges faced in traditional image compression methods and sets the stage for the objectives of the study, which include investigating the effectiveness of fractal geometry in compressing images while maintaining their quality. The study acknowledges the limitations of using fractal geometry in image compression and defines the scope of the research to focus on specific algorithms and techniques. The significance of the study lies in its potential to revolutionize image compression methods by leveraging the unique properties of fractal geometry. The structure of the thesis is outlined to guide the reader through the subsequent chapters, which delve deeper into the literature review, research methodology, findings discussion, and conclusion. The literature review chapter critically examines existing research on fractal geometry and image compression, highlighting key concepts, algorithms, and advancements in the field. By analyzing ten key studies, the chapter synthesizes the current state of knowledge and identifies gaps that this research aims to address. The research methodology chapter outlines the approach and tools used to investigate the applications of fractal geometry in image compression. Eight key components, including data collection, algorithm implementation, and performance evaluation, are detailed to provide transparency and rigor in the research process. The findings discussion chapter presents a detailed analysis of the results obtained from applying fractal geometry to image compression. By comparing the compression efficiency, image quality, and computational complexity of different fractal-based algorithms, insights are drawn on the strengths and limitations of each approach. The chapter also examines the impact of varying parameters on compression results and discusses practical considerations for implementing fractal geometry in real-world applications. In conclusion, this thesis summarizes the key findings, implications, and contributions to the field of image compression through fractal geometry. By evaluating the effectiveness of fractal-based techniques in reducing image size without compromising quality, this research opens new avenues for optimizing image compression algorithms. The study concludes with recommendations for future research directions and practical applications of fractal geometry in enhancing image processing technologies.

Thesis Overview