QP initialization and adaptive MAD prediction for rate control in HEVC-based multi-view video coding
Why this work is in the frame
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Bibliographic record
Abstract
Rate control is an important component of an end-to-end video communication system. Although rate control is not a part of a video coding standard, it is necessary for practical deployment. Currently, there are several proposals for rate control in the upcoming High Efficiency Video Coding (HEVC) standard, but there is no rate control scheme for HEVC-based multi-view extension. In this paper, we apply the newly recommended R-λ model-based HEVC rate control to the multi-view scenario, and propose two improvements. One improvement deals with Quantization Parameter (QP) initialization, and the other deals with adaptive Mean Absolute Difference (MAD) prediction. Results demonstrate the accuracy of the proposed methods, the resulting reduced fluctuation of instantaneous bitrate, as well as an improvement in rate-distortion performance compared to the R-λ rate control alone.
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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 | 0.000 | 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