Compression Performance

Compression performance in machine learning and signal processing focuses on minimizing data size while preserving information quality, crucial for deploying large models on resource-constrained devices and improving data transmission efficiency. Current research emphasizes techniques like quantization (including vector quantization), pruning (structured and unstructured), and the use of learned compression methods based on autoencoders, transformers, and implicit neural representations, often tailored to specific data types (images, video, text). These advancements are significant for reducing storage needs, bandwidth requirements, and computational costs in various applications, ranging from edge AI to large language model deployment and efficient data transmission in space missions.