Feature-bit-plane motion estimation using enhanced motion vector
Why this work is in the frame
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Bibliographic record
Abstract
This paper presents a fast block-based motion estimation algorithm based on a bit plane technique. This algorithm can produce the eligible candidates for finding the enhanced motion vectors. The proposed method translates the expensive 2D block-matching problem to a simpler 1D matching by choosing some blocks as eligible candidates and eliminating the others. The bit-plane idea combined with the enhanced motion vector leads to a novel motion estimation algorithm. This novel algorithm offers computational scalability through two parameters, so the speed/performance trade-off of the proposed method can easily be controlled. Experiments show that the proposed method is several times faster than the full search algorithm and it produces a better prediction performance. There are some cases in which existing motion estimation techniques obtain a false motion vector with a huge prediction error, while the proposed method can find an enhanced motion vector with an excellent accuracy.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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