Neural Video

Neural video compression aims to leverage deep learning to improve the efficiency and quality of video encoding and decoding, surpassing traditional methods like H.266/VVC. Current research focuses on developing efficient neural codecs using architectures such as hierarchical predictive coding, transformers, and conditional autoencoders, often incorporating techniques like low-resolution representation learning and feature modulation to enhance speed and compression ratios. These advancements hold significant promise for reducing bandwidth requirements and enabling real-time video processing on resource-constrained devices, impacting both cloud video services and mobile applications.