Abstract
The paper examines in detail the modern methods of image compression, focusing on how advanced algorithms are used in practical digital imaging systems. The study examines many compression methods, including LZMA, LERC, ZSTD and their mixed forms and compares how well they perform in terms of compression ratio, time required, memory efficiency and how much information entropy they keep. Machine learning methods for compression are used in the analysis, focusing on how they work with images from medical imaging as well as satellite data. Experiments are performed on standardized datasets, with the main goal of following the theoretical limits set by Shannon’s Source Coding Theorem. The study shows that using modern hybrid algorithms, it is possible to compress data by at least 4:1 and keep it safe, with LZMA and LERC combinations performing best when the data is subject to entropic constraints. The results show that using parallel processing leads to a 60% decrease in processing time when compared to traditional single-threaded methods. The results strengthen the theories and techniques needed for the next generation of compression systems, mainly for handling high-resolution images quickly.