Neural compression is a machine learning approach that reduces the size of data, such as images, video, audio, or other information, while attempting to preserve the details that matter most. Neural networks learn how to represent data using a smaller set of features and then reconstruct an approximation of the original information from that compact representation.
For example, an AI-powered camera can compress video into a smaller data stream before transmitting it to a cloud platform. The compressed representation requires less storage and bandwidth while retaining important visual information for later analysis. Neural compression is useful in image and video processing, edge AI, cloud storage, streaming, telecommunications, and systems where computing or network resources are limited.
