A multiresolution motion estimation technique with indexing
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Bibliographic record
Abstract
In the multiresolution motion estimation (MRME) techniques originally proposed by Zhang and Zafar, four MRME algorithms have been proposed. In one of these algorithms, the motion vectors in the low-pass subband are properly scaled and used as the final motion vectors for all the other subbands, and, in another, the properly scaled motion vectors in the first algorithm are used as predictions and further refined. The former algorithm requires a much lighter computational load and fewer coding bits for the motion vectors than the latter; on the other hand, the latter is able to provide a better MRME performance than the former. In this paper, we propose a new MRME technique that takes advantage of both of the above algorithms. In the proposed algorithm, the sum of absolute difference associated with each of the scaled motion vectors as in the first algorithm is calculated, and the result compared with the sum of the absolute values of the amplitudes of the wavelet coefficients within the motion block to be compensated. The outcome of the comparison decides if these scaled motion vectors are accepted as the final ones. For the coding of motion information, the motion vectors used for the prediction and their patterns of applicability to higher resolution levels, called the indices, are coded.
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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 | 0.001 | 0.001 |
| 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