Data Compression

Data compression aims to reduce the size of datasets while preserving essential information, a crucial task given the exponential growth of data in science and technology. Current research focuses on leveraging machine learning, particularly neural networks like transformers and autoencoders, alongside traditional methods like Huffman coding and Lempel-Ziv algorithms, to achieve higher compression ratios and improved efficiency, especially for complex data types such as images, videos, and sensor readings. These advancements are vital for managing the storage and transmission of large datasets in various fields, from cosmology and climate science to medical imaging and the Internet of Things, enabling more efficient data analysis and application deployment.