Compression Algorithm

Compression algorithms aim to reduce data size without significant information loss, a crucial task across diverse fields from image and video processing to large language model deployment. Current research emphasizes developing efficient algorithms for various data types, leveraging neural networks (especially Transformers), probabilistic graphical models, and quantization techniques like Kashin representation to achieve high compression ratios while preserving data fidelity. These advancements are vital for managing the ever-increasing volume of digital data, enabling efficient storage, transmission, and deployment of complex models on resource-constrained devices.