Variable Rate Compression

Variable rate compression aims to efficiently reduce data size while maintaining quality, adapting the compression level to the specific needs of the data. Current research focuses on developing single models capable of achieving variable compression ratios across diverse data types, including images, videos, point clouds, and text data from large language models, often employing transformer-based architectures or autoencoders with techniques like selective compression and multi-objective optimization. These advancements are crucial for managing the ever-increasing volume of data in various applications, improving efficiency in areas such as long-context language processing, high-resolution video streaming, and 3D point cloud processing. The ability to dynamically adjust compression levels offers significant improvements in resource utilization and performance across numerous fields.