A Novel Hierarchical Search Motion Estimation Algorithm
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
A new hierarchical motion estimation algorithm that has better performance than other conventional hierarchical algorithms will be presented here. In this technique several sub-optimal algorithms are utilized without increasing the complexity of the circuit, at the same time the computation cost is reduced significantly while the accuracy is kept very close to the full search algorithm. The reduction of the computation is obtained by calculating the distortion between the decimated block of the current frame and that of the previous frame. The motion vector is found hierarchically by searching the candidate motion vectors found from previous level of search. Each higher level search uses partial distortion functions with decreased ratio of decimation. To reduce the computation further, only part of the locations in the search area is considered. The proposed algorithm is suitable for applications such as HDTV and MPEG-2 where motion estimation accuracy is important. It is also suitable for real-time applications such as video conference and H.263 where encoding speed is critical.
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 | 0.001 | 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