A fast lossless compression scheme for digital map images using color separation
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, we present a fast lossless compression scheme for digital map images in the raster image format. This work contains two main contributions. The first is centered around the creation of a code book that is based on symbol-entropy. The second contribution is the introduction of a new row-column reduction algorithm. Our scheme proceeds as follows: we determine the number of different colors in a given map image. For each color, we create a separate bi-level data layer, one for the color and the second is for the background. Then, we compress each bi-level layer individually using the proposed method, which is based on symbol-entropy in conjunction with our row-column reduction coding algorithm. Our experimental results show that our lossless compression scheme achieved on average a compression equal to 0.035 bits per pixel which is better than most reported results in the literature or comparable to some. Moreover, our scheme is simple and fast.
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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 | 0.003 |
| Open science | 0.001 | 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