Rate Distortion Optimization

Rate-distortion optimization (RDO) aims to find the optimal balance between data compression (rate) and information loss (distortion) in various data types, from images and videos to 3D models and sensor data. Current research focuses on integrating RDO into neural network architectures, such as generative models (e.g., GANs, NeRFs) and convolutional neural networks (CNNs), to improve compression efficiency and quality, often employing techniques like entropy coding and quantization. These advancements are significantly impacting fields like video coding, image processing, and sensor data transmission by enabling more efficient storage and transmission of high-dimensional data while maintaining acceptable fidelity. The development of faster, more efficient RDO algorithms is a key area of ongoing investigation.