Hybrid de-interlacing algorithm based on motion vector reliability
Why this work is in the frame
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Bibliographic record
Abstract
This paper presents a hybrid de-interlacing algorithm that converts video from interlaced format to progressive format. This hybrid algorithm effectively combines two existing de-interlacing techniques: one that offers high vertical resolution and the other that is robust to erroneous motion vectors. The combination is based on a new measurement of motion vector reliability, so that the hybrid algorithm is dominated by the high-resolution technique if motion vectors are reliable; otherwise, it approaches the robust technique. Motion vector reliability is measured using the a posteriori probability of motion vectors. Experimental results show that this is an effective measure of motion vector reliability. The hybrid algorithm offers high spatial resolution without artifacts caused by erroneous motion vectors, and outperforms four of the best existing techniques in terms of peak signal-to-noise ratio.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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