Bibliographic record
Abstract
A new video standard called High Efficiency Video Coding (HEVC) has been recently finalized. In comparison with the H.264/AVC video coding standard, HEVC further improves the video coding rate distortion (RD) performance, but at the price of significant increase in its encoding complexity, especially in its motion estimation (ME) due to large block sizes and complicated block partition. To reduce the ME complexity in HEVC while maintaining its RD performance, in this paper we first formulate ME at the integer pixel level as a statistical inference problem and then propose a confidence interval-based ME (CIME) method. The proposed CIME method can be applied either on top of the existing fast search implemented in HEVC or on its own to replace the existing fast search implemented in HEVC. Experiments show that for the low-delay main test configuration of HEVC: 1) when applied on top of the existing fast search in HEVC, the proposed CIME method further reduces, on average, the integer-level ME time by 70% with only 1.0% increase in bit rate while maintaining the same reconstruction quality in PSNR and 2) when applied on its own to replace the existing fast search implemented in HEVC, the proposed CIME method achieves performance comparable with that of the fast search in HEVC reference software and better than that of the dynamic system fast algorithm proposed recently in the literature.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".