Wavelet Compression

Wavelet compression techniques aim to efficiently represent data by decomposing it into different frequency components, enabling reduced storage and faster processing. Current research focuses on integrating wavelet transforms with neural networks, particularly exploring learned lifting schemes and channel attention mechanisms to optimize compression performance for various data types, including images and volumetric maps. These advancements are improving the efficiency of applications ranging from image processing and robotics (e.g., efficient environmental mapping) to fault diagnosis in power electronics, demonstrating the broad applicability of wavelet compression across diverse scientific and engineering domains.