Quantitative Mottle Measurement Based on a Physical Model of the Spatial Contrast Sensitivity of the Human Visual System
Bibliographic record
Abstract
Print non-uniformity, or mottle, is an important factor in print quality. The ultimate judge of print quality is the printer or print buyer, so print quality measurement should be representative of human perception. Most methods that are currently available to systematically quantify print mottle do not consider eye response in the calculation. Instead, the user has to select appropriate scales for the analysis by comparing with separate visual ranking experiments for each new set of prints. We developed a method to process digital images of mottled black prints to provide a mottle index that takes into account eye response. The mottle indices obtained for a range of paper and board grades were compared with the results of separate visual rating experiments, and there was very good agreement between them. The mottle index outperformed other parameters also used for the quantification of mottle. Based on these results, the mottle index is deemed reliable enough to decrease the need for separate visual assessments by panels. The mottle index algorithm removes the need for the operator to make subjective choices on the appropriate analysis scales for sample sets where print uniformity is the dominant quality criterion. The proposed mottle measurement method allows systematic and objective quantification of mottle. The method can easily be implemented to analyze test prints using the analysis software we developed, and an appropriate desktop scanner that will require calibration to relate the greyscale to reflectance values.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".