Neural Representation for Video

Neural representations for videos (NeRVs) aim to efficiently encode and decode video data using neural networks, overcoming the challenges posed by high dimensionality. Current research focuses on improving encoding and decoding speeds, enhancing spatiotemporal consistency through architectures like pyramidal and multilayer networks, and optimizing parameter efficiency by leveraging techniques such as polynomial neural networks and decomposing static and dynamic video components. These advancements offer potential for significant improvements in video compression, restoration, interpolation, and other video processing tasks, leading to more efficient and higher-quality video applications.