The first thing I said was, “the more you compress something, the more processing power you’re going to need [to decompress it]”
I’m not removing the most computationally expensive part by any means and you are misunderstanding the process if you think that.
That’s why I specified:
The drawback is that you need a powerful computer and a lot of energy to regenerate those images, which brings us back to the problem of making this data conveyed in real-time while using low-power.
And again
But of course, that’s still going to take time to decompress as well as a decent spike in power consumption for about 30-60+ seconds (depending on hardware)
Those 30-60+ second estimates are based on someone using an RTX 4090, the top end Consumer grade GPU of today. They could speed up the process by having multiple GPUs or even enterprise grade equipment, but that’s why I mentioned that this depends on hardware.
So, yes, this very specific example is not practical for Neuralink (I even said as much in my original example), but this example still works very well for explaining a method that can allow you a compression rate of over 20,000x.
Yes you need power, energy, and time to generate the original image, and yes you need power, energy, and time to regenerate it on a different computer. But to transmit the information needed to regenerate that image you only need to convey a tiny message.