Lloyd-Max quantization-based priority index assignment for the scalable extension of H.264/AVC
Why this work is in the frame
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Bibliographic record
Abstract
A fast priority index (PID) assignment algorithm is developed for the MGS (Medium Grain Scalability) packets in the scalable extension of the H.264/AVC. The contributions of the paper are threefold. First, we formulate the index assignment problem as the quantization of the rate-distortion (R-D) slopes of MGS packets, and use the Lloyd-Max algorithm to find the optimal solution. The slope quantization index of a packet is used as its PID. The complexity of our method is much lower than existing method. Secondly, the quantization-based PIDs facilitate the comparisons of packets from different video streams. Video multiplexing results show that the overall PSNR can be improved up to 1 dB. Finally, we propose some real-time and adaptive implementations of the proposed method, which have the same performance as the offline method.
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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