What if you have an ML model that produces a vector from a given image. You have a set of vectors that correspond to bytes - for a simple example you have 256 "anchor vectors" that correspond to any possible byte.
To compress data an arbitrary sequence of bytes, for each byte, you produce an image that your ML model would convert to the corresponding anchor vector for that byte and add the image as a frame in a video. Once all the bytes have been converted to frames you then upload the video to YouTube.
To decompress the video you simply go frame by frame over the video and send it to your model. Your model produces a vector and you find which of your anchor vectors is the nearest match. Even though YouTube will have compressed the video in who knows what way, and even if YouTube's compression changes, the resultant images in the video should look similar, and if your anchors are well chosen and your model works well, you should be able to tell which anchor a given image is intended to correspond to.
To compress data an arbitrary sequence of bytes, for each byte, you produce an image that your ML model would convert to the corresponding anchor vector for that byte and add the image as a frame in a video. Once all the bytes have been converted to frames you then upload the video to YouTube.
To decompress the video you simply go frame by frame over the video and send it to your model. Your model produces a vector and you find which of your anchor vectors is the nearest match. Even though YouTube will have compressed the video in who knows what way, and even if YouTube's compression changes, the resultant images in the video should look similar, and if your anchors are well chosen and your model works well, you should be able to tell which anchor a given image is intended to correspond to.