NAB 2020, April 19 - 22, Booth N5329 – iSize Technologies, the London-based deep-tech company specializing in deep learning for video delivery, will be showcasing BitSave v.2, the next-generation ‘precoder’ for perceptual optimization of video content prior to subsequent encoding, at NAB 2020. This is the first time iSize is exhibiting at NAB.
BitSave v.2 is a pixel-to-pixel neural-network solution that optimizes video frames before the pixels reach the video compression software according to the latest advances in perceptual quality metrics. It enhances details of the areas of each frame that affect the perceptual quality score of the content after encoding and attenuates details that are not important.
This is designed to optimally balance perceptual quality against encoding bitrate. The solution is data-driven, trained on large image and video datasets, with no hand-crafted parameters.
It incurs a single-frame latency, can be used with any third-party video encoder with no changes in the encoding pipeline, and can run in real-time for up to 4K resolution.
iSize Technologies CEO Sergio Grce said, “It is predicted that by the end of 2020, more than 82% of internet traffic will be taken up by video, with consumers increasingly investing in and consuming higher quality video content. In response to this fast-moving market, iSize develops innovative codec independent software solutions that allows data-heavy video content to be compressed to a fraction of its original size, meaning content can be streamed faster and at a better quality.”
Extensive testing has shown its patent-pending AI features can make encoding up to 500% faster.
Since BitSave can be used alongside existing software, companies that have invested in compression software can use BitSave as an ‘add-on’ without having to shelve ongoing projects. The AI technology behind BitSave is fully codec independent, increasing the efficiency and performance of all the latest codec standards including AVC/H.264, HEVC/H.265, and VP9. This ensures seamless integration with existing media workflows.
BitSave is also unconstrained by standards because it produces a pixel output, which means it can use advanced neural networks and add multiple quality functions that cannot be achieved otherwise.
Ultimately, BitSave allows consumers to stream higher quality video, using less data or bandwidth, thus reducing the environmental impact of streaming. The devices used will also need to use less computing power to stream quality video, which means a longer battery life when watching content on the go.