Fast inter mode decision for HEVC based on transparent composite model
Bibliographic record
Abstract
In comparison with H.264/AVC, the newest video coding standard, High Efficiency Video Coding (HEVC), improves video coding rate distortion (RD) performance, but at the price of significant increase in its encoding complexity, due to its complicated inter mode decision process. In HEVC inter coding, the actual RD costs of all combinations of coding units (CUs), prediction units (PUs), and transform units (TUs) have to be computed and then the combination (i.e., mode) with the minimum cost is selected and encoded. To reduce the inter mode decision complexity in HEVC while maintaining its coding efficiency, in this paper, a fast inter mode decision method based on a newly proposed Transparent Composite Model (TCM) is developed. Spatially and temporally homogeneities are identified by TCM, and then used, together with spatiotemporal correlation between CUs, to determine PU and CU partitions so that cost computations of unnecessary modes can be skipped. Experimental results show that, for the low delay main test configuration of HEVC, our method reduces, on average, the encoding time by 60.71% with an insignificant loss in coding efficiency (1.06% BD-Rate increase).
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".