Specification of hierarchical-model-based fast quarter-pixel motion estimation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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