Neighbourhood-blocks motion vector estimation technique using pyramidal data structure
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Bibliographic record
Abstract
A pyramidal motion estimation technique that makes use of the motion correlation within a pyramidal level is proposed. In the proposed technique, motion vectors from neighbouring motion blocks are taken into consideration as possible candidates. This is done in lieu of scaling the motion vectors from the corresponding positions at the adjacent lower pyramidal level as the prediction motion vectors for the current pyramidal level (as performed in the conventional technique). Each of these candidate motion vectors is used as the prediction motion vector and refined, and the one that has the least matching distortion is chosen as the motion vector at the current pyramidal level. Compared to the conventional pyramidal motion estimation technique, the proposed method effectively overcomes the problem of propagation of false motion vectors. Simulation studies show that a substantial improvement is achieved in the performance, both in terms of the prediction mean square error and the number of coding bits for the motion vectors.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.009 |
| Open science | 0.001 | 0.001 |
| 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