Imagine you take a 580p lossless video. use red green and blue 3 bit dithering, lower some resolution, take out small triangular pieces, tweak the accuracy down, triangulate the remain data into a video file. Then train a double data type augmented dataset the first data type is for data regarding general estimation based morphic upscaling and piece analysis general estimation for filling in the blanks. So this data upscales by morphing in a higher resolution. The second data type is for handing bias. So rather than turning a face into a more generalist estimated upscale the video is tailored relative to a deeper data set.
Nvidia could make an architecture extension for ARM that inferred/rendered efficiently from this 2 data type augmented dataset.
This Technology could be licensed as an ARM extension for a few million dollars.
In Gaming on the next nintendo to come out in a few years time hopfully, It's key to remember that 4k could be done more efficiently if you used concentration point field 3d surfacing. by having a cache of point concentration graph information in 2d you could represent 3d surfacing in point concentration data meaning that you could lower the number of triangles needed working with per frame and lower the data accuracy optimally in sync with touch up data for 400p <-> 800p upscaled to 1k<->2k then a lower quality upscaler is used without touch up data to take it to 4k.
Tweak the surfacing for lumen cross Hardware raytracing lowering accuracy relative to what can later be upscaled.
Also having an engine which dynamically tweaks accuracy
for a higher frame rate.
This could mean that you could have some next gen nintendo 35watt system play any level of PS5 game looking roughly as good.