Bitgroup modeling of signal data for image compression
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Summary form only given. Binary variable order adaptive algorithms like the UMC of Rissanen (1986) and JBIG can be used to losslessly compress non-binary data by splitting the data into planes, each of 1 bit resolution, and passing each plane to a separate instance of the algorithm. The UMC algorithm operated in this way is the most powerful lossless signal data compressor the authors are aware of. We attempt to develop an understanding of why this approach is so effective. We investigate the common technique of Gray coding the data before splitting it into single-bit planes and passing to the modeler and coder, and compare it to a simple weighted binary coding. We then propose a non-binary pseudo-Gray code as a method of generating planes of resolution greater than or equal to 1 bit, and compare it with the other conventional methods. The algorithm to generate the pseudo-Gray code is much the same as that for the construction of a binary Gray code, except that instead of minimizing the Hamming distance between neighboring bit planes, we instead minimize the Euclidean distance between adjacent groups of bit planes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it