Massively parallel rate-constrained motion estimation using multiple temporal predictors in 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
Rate-constrained motion estimation (RCME) is considered to be the most time-consuming process of H.265/HEVC encoding. Massively parallel architectures, such as graphics processing units (GPUs), used in combination with a multi-core central processing unit (CPU), provide a promising computing platform to achieve fast encoding. However, the inherent dependencies in the process for deriving motion vector predictors (MVPs) prevent the parallelization of prediction units (PUs) processing. In this paper, we present a framework for performing a two-stage parallel RCME, in which the RCME of all the PUs of a frame can be calculated in parallel. A novel method is introduced to overcome the dependencies inherent to the derivation of MVPs. Multiple temporal predictors (MTPs) within the two-stage parallel RCME framework provide fine-grained parallelism encoding without significant BD-Rate penalty, compared to serial encoding. Experimental results show that our proposed approach achieves a BD-Rate improvement of over 1% as compared to state-of-the-art parallel methods providing similar time reductions.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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