Compression Network

Compression networks aim to efficiently reduce the size of data, such as images, videos, and speech, while minimizing information loss. Current research focuses on improving the rate-distortion trade-off using various architectures, including state-space models, transformers, and convolutional neural networks, often incorporating techniques like vector quantization and attention mechanisms to enhance efficiency and performance. These advancements are crucial for handling increasingly large datasets in various applications, from multimedia transmission to efficient deep learning model deployment, impacting both computational resource usage and bandwidth requirements.