Bibliographic record
Abstract
Continuous tone images must be halftoned to be displayed on binary output devices such as printers. Halftoning algorithms at low resolutions of the output hardware introduce textures into the resulting display. In this work we control halftoning texture by generating a threshold matrix from an image‐based texture. We demonstrate that processing textures by the adaptive histogram equalization algorithm approximates pixel distribution properties of traditional dither screens. Ordered dithering with the resulting threshold matrix enables us to define texture in the halftoned image. We control the appearance of this texture by a combination of the ordered dither algorithm with an error diffusion process. We present applications of texture‐based dither screens to both photorealistic and artistic rendering. In the case of photorealistic tone reproduction our technique preserves textures and edges of the original image. The ability to define an arbitrary texture enables us to introduce a variety of artistic effects, including embossing of images with textures and text, and approximation of the appearance of of conventional illustration media. We evaluate the resulting halftoning using multi‐scale edge distortion measures. Our quantitative evaluation closely corresponds to the visual observations.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".