Scalable High-Throughput Architecture for H.264/AVC Variable Block Size Motion Estimation
Why this work is in the frame
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Bibliographic record
Abstract
Variable block size motion estimation (VBSME) is a key part of the new H.264/AVC video coding standard. This has increased the demand for high performance VBSME architectures. This paper proposes a VLSI architecture for high throughput VBSME. The VBS calculation is done by combining the results of sub-block calculations to form the results for larger blocks. High motion vector throughput is achieved in two proposed implementations: one performing operations on a 1/spl times/4 set of pixels per cycle, and the second performing operations on a 1/spl times/16 set of pixels per cycle. Using these approaches, the architecture is able to produce motion vector results at a higher throughput than current VBSME designs, while providing a high level of scalability through adjusting the length of the processing element array.
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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