Residual Coding

Residual coding is a signal processing technique aiming to improve compression efficiency by encoding only the difference (residual) between an original signal and a prediction, thereby reducing redundancy. Current research focuses on integrating residual coding with neural networks, particularly autoencoders and conditional models, often within hierarchical or multi-scale frameworks to handle diverse data types like images, video, point clouds, and speech. This approach shows promise in achieving significant rate-distortion improvements across various applications, leading to more efficient storage and transmission of large datasets while maintaining high fidelity.