[TZubiri]

Of course it isn't

A text encoding uses 8bits per character on average, tokenization further compresses that

An image font would be 25 bits if 5x5, and most fonts are 12 pixels high

Of course it isn't efficient, this is a pricing inefficiency and a hack to exploit it (even the author describes it as an exploit)

[legel]

You are wrong.

Text tokens are high-dimensional vectors, not 8 bits per character. Every token has a deep embedding, e.g. 1024 float values per text token.

DeepSeek-OCR proved 10x+ compression from visual embedding of text, which was a groundbreaking result. [1]

Very cool to see OP's project hacking on this principle. It's still not lossless, as noted in the github, but is a promising research direction.

[1] https://github.com/deepseek-ai/DeepSeek-OCR/blob/main/DeepSe...

[Groxx]

I kinda wonder if it's extracting usable context from 2D proximity between lines? Normal text input wouldn't have that kind of information (though it could, and it's arguably just a lookahead/behind of N characters on average).

[deburo]

A token is probably not a single char, and an image is probably decomposed into tokens as well (and god knows how many tokens an image is decomposed into) which probably map to similar float-hungry vectors. Your counterargument could use a bit more flesh.

And we're talking about images of texts, not images that represent complex imagery such as a very detailed scene or what have you.

[gamblor956]

People really need to read their cites and not just the summaries.

The paper notes two things:

1) While the compression ratio for visual text is better than it is for regular text, but the absolute space required is still higher for the images. OPs were talking about the space required, not the ratio.

2) The results of the OCR must still be fed into a text-based LLM for linguistic processing. Otherwise, all you have achieved is turning an image into a bunch of text.

[nextaccountic]

Well, then we could presumably also add lossy compression to texts, without passing through images first

[TZubiri]

>Text tokens are high-dimensional vectors,

You are conflating tokens with embeddings.

Tokens fit in a single word, modern gpt uses a vocabulary with 200k possible values, which would fit into 18 bits.

Have a good one

[netsharc]

huh, what if the image encoding is 8 bits per R, G, B values of the pixel, then one can encode the same amount of text in less pixel dimensions (3 letters would need 1 pixel instead of three 12x12 pixels)

The top line can be the OCR-able instruction on how to decode the rest of the image, and the rest of the image would be random-looking colourful palette. It might not even need to use 8 bits per character, since ANSI is 7 bits/character.

[TZubiri]

then it's no longer an image, as the one in the github repo, you would be encoding the text as characters and sending it as an image.

You can achieve this by changing the extension of an image file from .bmp to .txt

Guys, not to be mean, but maybe chill with the state of the art research and go back to studying fundamentals.

[netsharc]

If you want to be a know-it-all poseur, at least back it up with data.

[TZubiri]

https://chatgpt.com/share/6a4a7ea3-65e8-83e9-8871-5041d97bb9...

Download the .bmp file and open it with 'edit' on windows or 'nano' on linux. You should see text. The bytes that precede the text are padding and a header that roughly 'tells how to interpret the data', in the sense that it describes the dimensions of the 'image'.

There's python code that shows how the file was generated, note that it just writes a string to the file and then wraps it with a .bmp header so that it can be interpreted by image viewing programs.

If you open that image with an image viewer you will see an image with colorful pixels. Whether you send it as an image or as text, the only difference lies in what you declare the file to be. For example if you change the extension, operating systems will "open" it with the appropriate program by default, but the data is the same, there's no difference between ASCII text and the encoding you describe where each subpixel describes one character, it's the same thing.

Your idea about using 7 bits instead of 8bits is more space efficient, but you'd need to decompress it so that the text is byte aligned. In terms of costs, you are not charged by the bandwidth consumed in transit, but by the computational cost, which wouldn't change as the text would need to be expanded to 8bits per char, (and then converted to the same token anyways)

Again don't want to be mean but these are 1st year comp sci topics, not at all related to LLMs. Hence why I recommend studying fundamentals more than papers that look like bleeding edge LLM research.