Specification of hierarchical-model-based fast quarter-pixel motion estimation
Bibliographic record
Abstract
We propose a robust and fast quarter-pixel motion estimation algorithm. This algorithm is an advanced version of the previously proposed model-based quarter-pixel motion estimation (MBQME). MBQME has many advantages in computational complexity, running speed, and hardware implementations. But it has the problem that it does not find the quarter-pixel positions that locate beyond the half-pixel positions. That is one of limitations of model-based motion estimation methods, and it leads to both peak-SNR degradation and bit-rate increase. To solve this problem, we propose a hierarchical mathematical model with minimum interpolations. Through this model, we can determine a motion vector at every quarter-pixel point, which is perfectly compatible with the quarter-pixel motion estimation method within international video coding standards such as MPEG-4 and H.264/AVC. The simulation results show that the proposed method yields almost the same or even better peak-SNR performance than that of full-search quarter-pixel motion estimation, with much lower computational complexity.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".