Rate Distortion Function

The rate-distortion function (RDF) quantifies the fundamental trade-off between data compression (rate) and information loss (distortion). Current research focuses on efficiently estimating the RDF for various data types, including images, videos, and even semantic information, employing techniques like the Blahut-Arimoto algorithm and neural network-based approaches, often leveraging optimal transport theory. These advancements improve the design of lossy compression algorithms by providing theoretical limits and enabling comparisons against optimal performance. Accurate RDF estimation has significant implications for optimizing data storage, transmission, and processing across diverse applications.