An Efficient Merge Mode with Motion Vector Difference Approach for Versatile Video Coding
Bibliographic record
Abstract
To address the problem in H.266/VVC that the newly proposed merge mode with motion vector difference (MMVD) cannot fully reflect the directional changes in inter prediction due to the increase of object flexibility, This paper proposes a directional improvement technique based on inter prediction of MMVD mode by expanding the number of directions included in the original technique from four to six. In order to reduce the computational complexity of the internal prediction of MMVD process and obtain better coding efficiency, the original step size of 32 precision was reduced to step size of 2 precision. The experimental results show that compared with the H.266/VVC standard algorithm, the algorithm in this paper can save 0.34% of BD-rate for luminance component Y and 0.30% of BD-rate for chrominance component U of the test sequence under the LDB configuration, while the coding and decoding time remains stable even decreasing under the step size.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".