Sub-partition reuse for fast optimal motion estimation in HEVC successive elimination algorithms
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Bibliographic record
Abstract
In the context of motion estimation (ME) for video coding, the rate-constrained successive elimination algorithm (RC-SEA) safely eliminates candidate motion vectors while preserving the optimal candidate chosen by the block matching algorithm (BMA). This paper describes a technique for reusing ME information from rectangular to square prediction units in order to reduce the search area without altering the optimal candidate chosen by the BMA. Our experiments show that, on average, when this optimization is combined with the RCSEA in the HEVC HM encoder reference software, the number of sum of the absolute differences (SAD) operations drops by 94.9%, resulting in a speedup of 6.13x in full search mode. Although identical coding decisions cannot be guaranteed when multiple optimal solutions exist, the average impact on BD-PSNR is 0.0002 dB.
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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 | 0.001 |
| 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