Lossless Compression of Maps, Charts, and Graphs via Color Separation
Bibliographic record
Abstract
Summary form only given. In this research, we present a fast and efficient lossless compression scheme for discrete-color digital map images, charts, and graphs stored in the raster image format. The proposed scheme determines the number of different colors in the given image and creates a separate bi-level data layer for each color. Then, the bi-level layers are individually compressed using the proposed algorithm. This scheme comprises two components: (i) a codebook; and (ii) our row-column reduction coding algorithm, RCRC. The codebook is a fixed-to-variable Huffman dictionary that is based on symbol entropy. The second component of our scheme is a new algorithm, the RCRC, designed to deal with those blocks that are not found in the codebook. Our experimental results show that our lossless compression scheme achieved an average compression equal to 0.035 bpp for map images and 0.03 bpp for charts and graphs. These results are better than most reported results in the literature. Moreover, our scheme is simple and fast.
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How this classification was reachedexpand
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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".