Soft Decision Quantization for H.264 With Main Profile Compatibility
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Bibliographic record
Abstract
In this paper, we study the rate-distortion (RD) optimization of the H.264 main profile encoding. Specifically, a soft decision quantization (SDQ) algorithm is developed based on the context adaptive binary arithmetic coding (CABAC) method in the H.264 main profile. Given motion prediction and quantization step sizes, the proposed SDQ algorithm is proved to achieve near-optimal SDQ for residual coding in the sense of minimizing the true RD cost when the weak adjacent block dependency utilized in CABAC is ignored for optimization. The SDQ algorithm is then used in conjunction with a general RD optimization framework to jointly design motion prediction and residual coding for H.264 main profile coding given previously coded reference frames. Experiments have been conducted based on the reference encoder JM82 of H.264 main profile. Comparative studies show that the joint design method achieves on average 10% rate reduction at the same PSNR when compared with the RD method in the H.264 main-profile reference software, with half of the reduction coming from the proposed SDQ algorithm, and 20% rate reduction at the same PSNR when compared with the RD method in the H.264 baseline-profile reference software.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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