A new rectangular partitioning based lossless binary image compression scheme
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, we propose a lossless binary image compression scheme that can achieve high compression ratio via partitioning the black regions (one's) of the input image into rectangles. After partitioning, the top-left and the bottom-right vertices of each rectangle are identified and the coordinates of which are efficiently coded. Three different routines are used in this research. The proposed scheme is targeting images, which contain graphs and tables with solid gridlines in the background on the one hand. While on the other hand it is suitable for text images of languages where many characters have dots "nuqta " on them such as Urdu, Persian, and Arabic with big fonts. The proposed scheme has outperformed CCITT run length coding, modified READ, and REC significantly. Also it is faster and simpler to implement than the method reported in A. Quddus et al (1999)
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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 | 0.001 | 0.000 |
| 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