Cost-Based Search Ordering for Rate-Constrained Motion Estimation Applied to HEVC
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
In the context of motion estimation for video coding, combining a successive elimination algorithm (SEA) with a motion estimation algorithm reduces the number of computed cost functions without impact on rate or distortion. The SEAs use the sum of absolute differences to eliminate motion vector candidates in the search area that cannot improve the current minimum. The novelty in this paper is that instead of relying on a static geometric pattern (i.e., like a spiral), we proposed a dynamic algorithm that creates a cost-based search orderings. A dynamic cost-based search ordering not only improves elimination but also allows for early termination which removes, on average, 61% of the block-matching loop iterations performed by the rate-constrained successive elimination algorithm (RCSEA). Our experiments show that the proposed solution is 5 times faster than the high efficiency video coding (HEVC) HM encoder software in full search mode with a 0.02% impact on BD-Rate. This is twice the speed of the HEVC HM software encoder using only the RCSEA.
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 | 0.001 |
| Science and technology studies | 0.001 | 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