Study of the usage of fractal compression algorithms for information systems that handle large volumes of data and require high compression ratios while preserving image quality

Abstract

This work investigates the effectiveness of applying fractal compression algorithms in information systems that process large volumes of data and require high compression ratios while maintaining image quality. The principles of fractal algorithms, which utilize mathematical models to detect self-similar structures in data, are described, providing high compression ratios without significant loss of quality. A comparison of fractal methods with other popular compression methods, such as JPEG and Wavelet Transform, is conducted, evaluating their effectiveness based on performance, compression ratios, and the quality of restored images. It is found that fractal algorithms are promising for systems that need to preserve high-quality images with minimal quality loss, especially under conditions of limited storage resources and network bandwidth. Recommendations are developed for implementing fractal algorithms in information systems to improve the efficiency of managing large volumes of data.