Optimal Stopping Theory-Enabled VVC Intra Prediction with Texture
Bibliographic record
Abstract
Versatile Video Coding (VVC) introduces the new quad-tree with a nested multi-type tree (QTMT) block division structure, which increases the flexibility of block division, the more complex block division structure increases the coding complexity of VVC by nearly 26 times compared with High-Efficiency Video Coding (HEVC). Therefore, it is urgent to reduce the coding complexity of VVC. In this paper, we propose a fast CU division method based on optimal stopping theory and block texture decision. Firstly, by analyzing the division depth of the Coding Tree Unit (CTU) at the same position as neighboring frames, we use the optimal stopping theory to determine the optimal division layer of the current CTU, to terminate the division process in advance. Then, by judging the texture direction of the current Coding Unit (CU), the calculation of several CU division methods is selected to be skipped, thus reducing the computational effort of coding. The experimental results show that the coding time of this scheme is reduced by 45.65% on average, while the BDBR only increases by 1.64%.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".