Optimized Pattern Partitioning for Multi-Pass Printing: PARAOMASKING
Bibliographic record
Abstract
In halftone-driven imaging pipelines focus is often placed on halftone pattern design as the main contributor to overall output quality. However, for sequential or cumulative imaging technologies, such as multi-pass printing, an important element is also pattern partitioning - how the overall halftone pattern is divided among the different partial imaging events such as printing passes. Partitioning is usually designed agnostically of the halftone pattern, making it impossible to optimize for the joint effect of halftone and partitioning. However, even a good halftone pattern coupled with a good partitioning scheme does not guarantee well partitioned halftones and can impact image quality attributes. In this paper a novel approach called PARAOMASKING is presented that benefits from the pattern-determinism of PARAWACS halftoning and proposes a partitioning scheme for multi-pass printing such that optimality is also obtained for partitioned halftones. Results - both digital and printed - show how it can lead to significant improvements in partial pattern quality and overall pattern quality. Consequently, output attributes such as grain, coalescence and pattern robustness are improved. The focus here is on blue-noise pattern preservation but the approach can also be extended to other objectives, e.g., maximizing per-pass clustering.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".