An analysis-compression technique for black and white documents
Bibliographic record
Abstract
This paper presents an analysis and compression technique that can be used for both lossy and lossless compression of black and white documents simultaneously. It is assumed that an application-dependent analysis technique is employed to produce weighting coefficients that allow ordering of the bits according to their significance. Like the algorithm described in the JBIG standard, the proposed algorithm consists of high order statistical modeling and adaptive arithmetic coding. However, our modeling techniques are more sophisticated in the sense that they are adaptive both locally and globally. The conditioning region of support used for the generation of the states is determined based on the global statistics of the input image, and the states and associated probabilities are adapted to the local statistics. Moreover, our algorithm is naturally suitable for progressive transmission since the output bit stream can be truncated anywhere, leading to the best possible approximation given a bandwidth constraint. Experimental results reveal that the proposed algorithm not only achieves high near-lossless compression performance but also outperforms JBIG when used for lossless compression.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".