A fast three-step search algorithm by the utilization of multilevel vector partial sums
Why this work is in the frame
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Bibliographic record
Abstract
Due to the high computational requirement of the full-search algorithm for block motion estimation, fast block motion estimation algorithms are needed for real-time implementations of the video coding standards. Recently, a three-step search algorithm for block motion estimation has been proposed in the literature. In this paper, a fast three-step search algorithm is proposed to further reduce the computational complexity of the three-step search algorithm with no loss of accuracy. By using a multilevel vector partial sums and lower bounds in the proposed algorithm, a large number of possible candidate motion vectors are discarded while still retaining the optimal motion vector of the three-step search algorithm. It is shown that not all the levels of partial sums and lower bounds are needed. A method to select these vector partial sums and lower bounds are also presented. Simulations of the proposed algorithm are carried out for various benchmark video sequences and the results demonstrate that the new algorithm can reduce the computational complexity of the three-step search algorithm by 20 to 60 percent with no loss of 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 | 0.000 |
| 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