Rate Distortion

Rate-distortion theory studies the fundamental trade-off between the compression rate of data (e.g., images, videos, model parameters) and the resulting distortion or loss of information. Current research focuses on optimizing this trade-off in various applications, employing techniques like variational autoencoders (VAEs), Blahut-Arimoto algorithms, and determinantal point processes to achieve efficient compression while minimizing information loss. This field is crucial for advancing data compression, video coding, and machine learning, particularly in resource-constrained environments like mobile devices and distributed learning systems, by enabling efficient transmission and storage of large datasets. Furthermore, it provides a theoretical framework for understanding the limits of information processing in biological and artificial systems.