Pyramidal motion estimation techniques exploiting intra-level motion correlation
Why this work is in the frame
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Bibliographic record
Abstract
The conventional pyramidal motion estimation technique, although capable of reducing the heavy computational load as required by the full search block matching algorithm, has the problem of propagating false motion vectors. In this work, pyramidal motion estimation techniques that exploit the intra-level motion correlation to overcome this serious drawback are presented. Instead 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 is done in the conventional technique, the prediction motion vectors are generated through either median filtering or linear prediction of a set of neighboring motion vectors. Various pyramidal data structures are employed to test the proposed techniques. Simulation results show that the proposed techniques not only improve the prediction performance, but also result in a more consistent motion field. It is further shown that this improvement in the performance is achieved with negligible extra computational load.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.000 | 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