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- #ASSIGN HOTKEY QUOD LIBET VOLUME GENERATOR#
- #ASSIGN HOTKEY QUOD LIBET VOLUME ANDROID#
- #ASSIGN HOTKEY QUOD LIBET VOLUME SERIES#
#ASSIGN HOTKEY QUOD LIBET VOLUME ANDROID#
Lyra is focused on Android and requires a bunch of Java cruft to build and needs debugging. A few codecs have emerged as particularly adept at this specific class: Speex, Opus, and the latest, Google’s Lyra, a deep learning enhanced codec. Speech is generally a special class of audio files where compression quality is rated more on intelligibility than on fidelity, though the two related the former may be optimized at the expense of the latter to achieve very low data rates. DALL♾ 2‘s 1024×1024 output size should yield in 144,624:1 compression. My technique yields a compressed representation of 174 bits or 9,039:1 compression. WebP “low quality” compressing the 256×256 image yields a file of 146,080 bits or 10.77:1 compression.
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Using the DALL♾ mini output as the basis for computing compression rather than the input image which could be arbitrarily large, we have 256×256×8×3 bits output = 1,572,864 bits to represent the output image raw. If you saw the original scene and 20 years later, someone said, “hey, remember that time we saw the cat sitting on a wooden bench in Varna, look, here’s a picture of it!” and showed you this picture, I mean aside from the funny looking cat like blob, you’d say “oh, yeah, cool, that was a cute cat.” It is clearly a lossy conversion, but here’s the thing: so too is human memory lossy. That’s a pretty compact representation of an image! The model has been trained to understand a correlation between widely shared semantic symbols and elements of images and can reduce an image to a human-comprehensible, compact textual representation, effectively a lossy coding scheme referencing a massive shared codebook with complex grammatical rules that further increase the information density of the text.ĭecoding those 174 bits back to the original text, we can feed them into an image generating generative AI model, like DALL♾ mini and we get our original image back by reversing the process leveraging a different semantic model, but one also trained to the same human language. This is a cat sitting on top of a wooden bench which we can LZW compress assuming 26 character text to a mere 174 bits or 804D22134C834638D4CE3CE14058E38310D071087.
#ASSIGN HOTKEY QUOD LIBET VOLUME GENERATOR#
There are far more powerful AI systems available, but we’ll use one that allows normal people to play with it, caption generator on github: Next we use AI to reduce the image to a symbolic semantic representation.
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We can start with the a generic image from Wikipedia: So now lets try a little AI assisted vector quantization of images. If a person skilled at drawing were to attempt to represent this coded reference visually, it is likely the result would be recognizable to others as a representation of the text that is, the text is an extremely compact symbolic representation of an image.
#ASSIGN HOTKEY QUOD LIBET VOLUME SERIES#
The underlying mechanism is a sort of vector quantization where the text represents a series of vectors that semantically reference complex culturally shared elements that form a type of codebook. I’m sure each person reading this develops an internal model, likely some combination of a snug, warm indoor Christmas scene while outside a storm raged, or something to that effect derived from the shared cultural semantic representation: a scene with a great deal of detail and complexity, despite the very short text string. It was a dark and stormy night and all through the house not a creature was stirring, not even a mouse. Here disclosed is a novel compression technique I call Deep Learning Semantic Vector Quantization (DLSVC) that achieves in this sample 9,039:1 compression! Compare this to JPEG at about 10:1 or even HEIC at about 20:1, and the absolutely incredible power of DL image compression becomes apparent.īefore I disclose the technique to achieve this absolutely stunning result, we need to understand a bit about the psychovisual mechanisms that are being exploited.