Lossless image coding via one-dimensional grammar based codes
Bibliographic record
Abstract
Lossless image coding is considered from an information theoretic point of view. Three new coding schemes are proposed. In the first coding scheme, an image is first scanned in a quadrant-by-quadrant manner and then encoded by using a one-dimensional grammar-based code which has been developed by Yang and Kieffer (2000) and is called the improved sequential algorithm (or simply the YK algorithm). In the second coding scheme, an image is first predicted by using a context template then scanned in a quadrant-by-quadrant manner, and finally encoded by using the YK algorithm. In the third coding scheme, an image is first scanned in a quadrant-by-quadrant manner and then encoded by using a modified YK algorithm, which also includes a 2D arithmetic code as an option to remove local 2D redundancy. Because of the nature of the YK algorithm and the scanning method, all three coding schemes can remove effectively global redundancy existing in images. Indeed, it is proved that all three coding schemes are universal and outperform asymptotically finite 2D block code and any finite context 2D arithmetic code as the image size gets larger and larger. For small images, however, the second coding scheme is slightly more effective in removing local redundancy occurring in images than does the first coding scheme, and the third one is the best among the three. Simulation results on bi-level images confirm our theoretic results: for images of size 512/spl times/512, our results are comparable with those afforded by JBIG1; for some images of size 1024/spl times/1024, our results are better than those afforded by JBIG1.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".