A near exact image expansion scheme for bi-level images
Why this work is in the frame
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Bibliographic record
Abstract
Exact bi-level image expansion techniques are required for a wide range of applications such as cartography, calligraphy, medical images, remote sensing, and satellite imagery. Among the methods proposed in the literature are (a) pixel replication; (b) area sizing; (c) interpolation and spline methods; and (d) DCT-based techniques. All these methods generate distortion and noticeable degradation in the quality of images especially around edges. We introduce a new image expansion scheme that produces significantly improved expanded and/or reduced images and maintains high quality edges. This scheme uses an elaborate look-up table that is based on look-ahead-and-back procedures for each pixel and maintains a memory of the pixels' chain code connectivity. The experimental simulation results show that the resized images of the proposed scheme are aesthetically and objectively much better than those of the other methods.
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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 | 0.002 |
| 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