A critical analysis of dithering algorithms for image processing
Why this work is in the frame
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Bibliographic record
Abstract
The authors present a critical comparison of three dithering algorithms, ordered dither, error diffusion and dot diffusion, that are based on the multichannel model of the human visual system. The techniques are analyzed and compared quantitatively with particular emphasis on finding a suitable criterion to measure the performance. It is found that all the algorithms well preserve the power spectra of the original images in the low-frequency regions. The ordered dither and dot diffusion methods generate grid patterns in the frequency domain leading to undesirable texture and moire patterns in the spatial domain. In contrast, grid patterns are absent in the error diffusion method. This perhaps explains why the error diffusion algorithm in general produces better dithered images than the others. This analysis approach provides a useful framework for a better understanding and further development of dithering techniques.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
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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) | 0.001 | 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