Neural compression is a term from the fields of Artificial Intelligence, Big Data and Smart Data, and Digital Transformation. It describes methods with which large amounts of data are efficiently reduced, or compressed, using artificial neural networks, without losing important information.
Instead of conventional compression methods like ZIP or MP3, neural compression uses the learning ability of modern AI models. These models recognise which parts of the data are truly important and remove what is not strictly needed. This allows, for example, images, videos, or text files to be reduced in size so that they require less storage space and less transmission capacity, while at the same time preserving quality.
A clear example: In a company, surveillance videos from production are to be stored. Thanks to neural compression, these videos can be significantly reduced in size, allowing them to be transmitted faster and stored more cheaply – all without losing important details for later analysis. This not only saves costs but also makes working with huge amounts of data much more efficient.
Neural compression is therefore one of the modern tools for keeping an overview and control of the constantly growing flood of information in the age of Big Data and Artificial Intelligence.
Deutsch 
English (UK)