What happens when the information runs out

dont forget the data itself is already a compression of reality. text data has lost tone, situational context, emphasis, etc.

then youve also got different data that yields the roughly same information that isnt exact but serves the same purpose. for example, birthday vs age; one can be used to infer the other although it is more accurate moving in one direction than the other.

youve got resolution of data. for example, A specific hexcode value vs "deep navy blue" vs "blue". sometimes the signal is low resolution but the data type is high resolution and you end up with some tells like when a lot of date data is the 1st of the month or 1st day of the year; implying the signal was at the month level or year level, no day signal available. which to call back an earlier example, can be caused by getting a signal for age but storing it in a birthday data type field

to your point, often the "data" signal is not a single field but set, and then youve got your source updating every month or year, and its not always the same format or exact set, maybe it was collected differently or two or more different sources contributed. its hard to apply any rules without assessing the type of data being considered. newer data that is missing older data, sometimes you carry the old pieces forward and sometimes you dont. sometimes you change the shape of the data by inferring events. job title changed? well the old job title isnt wrong data, its just not their current title and now can be moved to a past job titles" field. maybe past job titles arent something that add value to your use case and it can be dropped, etc.

So there is some things that instinctively don't sound correct here.

A frame may not contain the info to enhance, maybe it's too blurry... but a series of frames can (I'm pretty sure) between them have enough info to deblur an object in the background, it really helps to know the focal length, and distance maps...

My only point is information in a video is more than the sum of the information of it's frames, it also captures delta, not just from frame to frame, but relevant frame to relevant frame. That's a fair bit to work off of.

I do agree with our general point when information is 'absent'. I disagree how much absent information is in a video which can't be reconstructed by this delta. It certainly isn't zero, or 100%.

Inventing colors from a grayscale is a good example of absent information and of where this goes awry. Deblurring/blurring operations, less so.

This is generally true. What I find fascinating is the application of information that is not present in the data but that can be confirmed by the data. I’m thinking specifically of deblurring algorithms people use to recover text. If you can guess what font is being used in a screengrab with, say, someone’s bank account blurred out, you can take the i formation contained in the downloaded font and try combinations until you match the blur algorithm that may also be information not contained in the image.

What’s also crazy, is that even if all you do is black out the text, kerning alone can still be used to guess the correct text with some degree of accuracy. I worked on redaction software at one point, and these were very much the types of things we thought about when dealing with text.

I don't know what the author thinks this could have meant, other than "give me the original". Making up details is what he is asking it to do.

True, you can't see the eyebrows in the original, but you also can't see (IMO) an absence of eyebrows and since far more people have eyebrows than not, I would be extremely surprised if it had come up with a version without them.

https://www.youtube.com/watch?v=JMIHNiR3CP8