Efficient H.264-to-HEVC Transcoding Based on Motion Propagation and Post-Order Traversal of Coding Tree Units
Bibliographic record
Abstract
In this paper, we propose a fast H.264-to-HEVC transcoder composed of a motion propagation algorithm and a fast mode decision framework. The motion propagation algorithm creates a motion vector candidate list at the coding tree unit (CTU) level and, thereafter, selects the best candidate at the prediction unit level. This method eliminates computational redundancy by pre-computing the prediction error of each candidate at the CTU level and reusing the information for various partition sizes. The fast mode decision framework is based on a post-order traversal of the CTU and includes several mode reduction techniques. In particular, the framework permits the early termination of the rate distortion cost computation, a highly complex task, when a mode is unpromising. Moreover, a novel method exploits the data created by the motion propagation algorithm to determine whether a coding unit must be split. This allows the pruning of unpromising sub-partitions. Compared with a cascaded pixel-domain transcoding approach, the experimental results show that the proposed solution using one reference frame is on average 8.5 times faster, for an average Bjøntegaard delta-rate (BD-Rate) of 2.63%. For a configuration with four reference frames, the average speed-up is 11.77 times and the average BD-Rate is 3.82%.
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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 | 0.000 |
| 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) | 0.001 | 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".