Just when you think something has been optimized, tweaked, analyzed and the problem solved – this industry will often surprise you with a better, stronger, faster version (hello ‘Six Million Dollar Man’).
Take what many call the pedestrian task of optimizing and obtaining the best encode of a given piece of content. Today’s streaming costs are no joke, especially when you offer a sizable library of content.
At Akta Tech, AI is changing video encoding the way autopilot changed flying: fewer guesses, more outcomes.
Most “optimized” #encoding still relies on choosing global settings (bitrate/CRF) and hoping they work across wildly different scenes. Our latest white paper breaks down why that approach wastes bandwidth on easy content and risks artifacts on complex shots—and why the next leap is VMAF-driven, per-scene optimization.
The headline: by predicting #VMAF at the scene level (instead of running exhaustive encode matrices), you can automate encoding decisions and still hit quality targets—fast.
A couple of proof points we highlight:
~12× faster optimization
<1 VMAF point error at the 99th percentile
~47–48% file size reduction while matching or improving quality in examples
If you’re rethinking #streaming efficiency in 2026, this is the direction: optimize for what viewers see, not just what your encoding settings say.
Please drop us a note at sales@akta.tech if you’d like to explore and discuss.