Dependency-Aware Unequal Error Protection for Layered Video Coding
Why this work is in the frame
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Bibliographic record
Abstract
Layered video coding standards encode a high-quality video into multiple layers of unequal importance. Dependent layers that provide higher quality rely on their respective reference layers for successful reconstruction of transmitted video frames. Hence, if a video packet in a reference layer is corrupted or lost during transmission, all its dependent layers cannot be reconstructed successfully, and the resources consumed to transmit them are wasted. To address this problem, unequal error protection (UEP) techniques have been proposed to provide protection to each layer according to their importance. Nonetheless, the importance of a piece of video content is determined by not only the layering structure, but also visual features and encoding decisions. In this paper, we look deeper into the coding and prediction structure of layered encoded videos and model the the dependency among macroblocks and submacroblocks (the finest processing units of H.264 video coding standard) as a weighted graph. Based on this graph, we propose a dependency-aware UEP model that protects macroblocks according to their importance. Our simulation results show that the proposed UEP model outperforms the conventional UEP models for layered SVC videos by 3.76 dB of peak signal-to-noise ratio (PSNR) when the channel packet loss rate is as high as 28%.
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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