Thesis Abstract

Abstract
This thesis examines the utilization of fractal geometry in the domain of image compression, exploring its potential applications and implications. The study is motivated by the ever-increasing demand for efficient compression techniques in various fields, particularly in the realm of digital image processing. Fractal geometry offers a unique approach to image compression by leveraging self-similarity and iterative transformations to represent complex images in a compact manner. Through a comprehensive literature review, this research investigates the theoretical foundations of fractal geometry and its relevance to image compression. The methodology employed involves analyzing existing image compression algorithms based on fractal geometry, as well as conducting experiments to evaluate their effectiveness compared to traditional methods. The findings reveal the strengths and limitations of fractal-based image compression techniques, shedding light on their performance metrics, computational complexity, and visual quality. The discussion delves into the implications of these findings for practical applications, highlighting potential areas for further research and development. The conclusion summarizes the key insights gained from this study and provides recommendations for future exploration in the field of fractal geometry and image compression. Overall, this thesis contributes to advancing the understanding of how fractal geometry can be harnessed to optimize image compression algorithms, opening up new avenues for innovation in this vital area of digital technology.

Thesis Overview