Optimal Stopping Theory-Enabled VVC Intra Prediction with Texture
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
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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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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