A multi-frame and multi-slice H.264 parallel video encoding approach with simultaneous encoding of prediction frames
Bibliographic record
Abstract
This paper describes a novel multi-frame and multi-slice parallel video encoding approach with simultaneous encoding of predicted frames. The approach, when applied to H.264 encoding, leads to speedups comparable to those obtained by state-of-the-art approaches, but without the disadvantage of requiring bidirectional frames. The new approach uses a number of slices equal or greater than the number of cores used and supports three motion estimation modes. Their combination leads to various tradeoffs between speedup and visual quality loss. For an H.264 baseline profile encoder based on Intel IPP code samples running on a two quad core Xeon system (8 cores in total), our experiments show an average speedup of 7.20×, with an average quality loss of 0.22 dB (compared to a non-parallelized version) for the most efficiency motion estimation mode, and an average speedup of 7.95×, with a quality loss of 1.85 dB for the faster motion estimation mode.
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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 | 0.000 |
| 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".