AI-based video compression for emerging technologies

Deep Render combines the fields of artificial intelligence, statistics and information theory to unlock the fundamental limits of video compression.The best data compressor known to mankind is the human eye, with compression ratios at least 2,000 times better than anything developed to date.

Our Biological Compression technology mimics the neurological processes of the human eye through a non-linear, learning-based approach, creating an innovative class of highly efficient compression algorithms.

By building an entirely new foundation for compression, avoiding the limitations of current codecs, our objective is to develop a video compression approach 80% more efficiency than the state-of-the-art.With 85% of all internet traffic being video data, growing exponentially, bandwidth supply is being used up at an unsustainable rate.

Even worse, emerging video technologies such as VR-streaming, Medical and Satellite Imaging, and Autonomous driving are bottlenecked by the unavailability of sufficient bandwidth.

Further, the amount of energy used and CO2 generated with online video is now being recognised as a major problem.If the EU Digital Single Market and economic growth are to be delivered, and Climate Change obligations met, a more efficient compression system is vital to free up bandwidth and reduce energy usage.

Our value proposition is simple, by reducing file sizes by 80%, we directly increase the bandwidth supply of the internet by a factor of 5, thus reducing data transport and storage requirements, reducing energy usage and CO2 emissions.Initially, the end-users of our technology will be content delivery networks, online streaming services and media production organisations.

The video encoding market is estimated to be worth €1.5Bn a year.

Our collaboration, including TU Wien and Contentflow (end-user), will develop, demonstrate and pilot the codec in a streaming service and begin extending the codec to new high growth, high value and high need markets.