Learning Based Compression

Learning-based compression leverages machine learning to achieve efficient data reduction, aiming for high compression ratios while minimizing information loss. Current research emphasizes autoencoders, transformers, and normalizing flows, often incorporating techniques like vector quantization and sparse representations to optimize compression performance across diverse data types (images, audio, scientific datasets). These advancements are crucial for managing the ever-increasing volume of data in scientific research, communication networks, and other fields, enabling more efficient storage, transmission, and analysis. The development of efficient and robust compression methods is particularly important for real-time applications and resource-constrained environments.