Statistical Learning Based Fast Mode Selection Scheme For H.264/AVC Inter Prediction
Why this work is in the frame
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Bibliographic record
Abstract
H.264 adopts variable block size motion estimation and Rate-Distortion-Optimization based mode decision to improve video quality and compression ratio.These techniques have made H.264 better than other existing video coding standards.However,they are computationally intensive and time-consuming.In this paper,a fast mode selection scheme is proposed for H.264 inter prediction.Firstly,the first few frames are encoded and thresholds are acquired through a statistical learning process.Then,for the rest of frames,motion estimation and mode decision are only performed for the candidate modes which are selected with the proposed fast mode selection scheme.The proposed approach is applicable to all existing motion search algorithms.Besides,thresholds are on-line computed separately for each sequence.Results show that the total encoding time is saved by 57.2% on average with negligible video quality degradation.
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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 | 0.000 |
| 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