<title>Error diffusion with blue-noise properties for midtones</title>
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In this contribution, a new error-diffusion algorithm is presented, which is specially suited for intensity levels close to 0.5. The algorithm is based on the variable-coefficient approach presented at SIGGRAPH 2001. The main difference with respect to the latter consists of the objective function that is used in the optimization process. We consider visual artifacts to be anomalies (holes or extra black pixels) in an almost regular structure such as a chessboard. Our goal is to achieve blue-noise spectral characteristics in the distribution of such anomalies. Special attention is paid to the shape of the anomalies, in order to avoid very common artifacts. The algorithm produces fairly good results for visualization on displays where the dot gain of individual pixels is not large.
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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 | 0.000 |
| 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 it