Comments (3)
For avoiding compression artifacts, we need someone who has experience with the codecs involved, or else some experimentation, to determine the spatial correlation of the different distortions. I'm actually not sure that deliberately breaking apart the areas of contiguous color is a good idea -- then it's harder for the encoder to encode the shape of that area, so it might end up having to compromise on the color instead. Also, another thing to consider is to try minimizing the inter-frame difference, so that there's less data to compress in the first place.
For redundancy, we're looking for an error-correction scheme with a special property: that it can deal with many small errors, rather than one big error. This is a rather unusual property; neither Hamming nor Reed-Solomon does this. Input from computer scientists is needed.
When I tried to encode data into videos, I tried to use QR codes instead of raw bytes. I think it worked quite well to deal with the compression issues, since QR codes have ECC built in, but I had problems with extracting them in the right order and actually putting them back together into a file: https://youtube.com/watch?v=HzU69Dm-BEs
The sound channel isn't trivially composable with the main data, and in any case it can store a fraction of the data that the main video can. But what that can be used for is to store a checksum of the data, so that errors in decoding can be detected. One strategy for correcting them could be to try again with a higher-resolution version of the video.
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I came here for this. The efficiency of storage could be greatly improved. Also, building in some redundancy would allow to play with higher risk palette for optimizing the final storage %
Another two things to consider:
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The same bytes could be represented by different colours, and the colour would be chosen based on neighboors in order to reduce potential compression artefact (ex: not have 2 shades of red next to each others)
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The sound channel could be also used for additional bandwidth
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good
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