Specification of the Geometric Regularity Model for Fuzzy<i>If-Then</i>Rule-Based Deinterlacing
Bibliographic record
Abstract
A fuzzy if-then rule-based intra-field deinterlacing method using geometric duality is presented in this paper. The proposed method is a content-based hybrid scheme switching between the well-known edge-based linear average method and the proposed geometric duality-based deinterlacing method. Conventional deinterlacing methods usually employ edge-based interpolation techniques within pixel-based estimations. However, they are somewhat sensitive to noise and intensity variations in the image. Moreover, their performance is visually unacceptable due to their failure to estimate edge direction. To reduce this sensitivity, the proposed algorithm investigates features from low-resolution images, and applies them to high-resolution images to calculate the missing pixels. We analyzed properties of the missing pixels and modeled them using geometric regularity. Depending on the features of the region, the missing pixels were interpolated in different ways. The proposed algorithm is computationally feasible and promises to be a good candidate for a low-cost hardware interpolator.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".