Low Redundancy Layered Multiple Description Scalable Coding Using The Subband Extension Of H.264/AVC
Why this work is in the frame
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Bibliographic record
Abstract
The task of broadcasting video in wireless environments requires high coding efficiency in addition to reliable error resilience techniques. Existing solutions are set to tackle either coding efficiency and bandwidth utilization on the one hand, or error resilience and recovery from packet delay or loss on the other. We propose a novel combined approach to deal with the problem of video broadcast over wireless networks by offering a layered multiple description scalable coding (LMDSC) technique using the subband extension of H.264. This approach combines the high coding efficiency and layered structure of the subband extension of H.264/AVC along with the highly error resilient performance of multiple description coding (MDC) while virtually eliminating all redundancy between the transmitted video streams. Performance evaluations show that when faced with the same amount of packet loss, our approach achieves significant improvement in PSNR over existing methods.
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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 | 0.001 |
| 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