Lossy Compression

Lossy compression techniques aim to reduce data size by discarding less important information, balancing compression ratio with data quality. Current research focuses on improving compression ratios and reconstruction quality across diverse data types (images, audio, scientific simulations, and neural network models) using methods like neural networks (autoencoders, transformers, and generative models), tensor decomposition, and novel loss functions tailored to specific data characteristics. These advancements are crucial for managing the ever-increasing volume of data in scientific research and practical applications, enabling efficient storage, transmission, and processing of large datasets. The development of compressed-domain operations further enhances the utility of lossy compression by enabling computations directly on compressed data, reducing computational overhead.