An End to End Quality Optimized Error Control for Wireless Video Transmission
Why this work is in the frame
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Bibliographic record
Abstract
Intra refresh is a very common tool to improve the error resilience performance for video encoding.Too many intra macroblocks will harm the compress efficiency.To achieve the optimal end to end quality for real video transmission system,it is crucial to make a tradeoff between intra refresh rates and compress efficiency.In this paper,by analyzing the affection of other factors on video qualities,i.e.quant parameter(QP) and channel conditions,we propose a cross layer based scheme to jointly optimize the intra refresh rate,QP,and adaptive modulation and coding(AMC) scheme at the physical layer to minimize the video distortion at the receiver.The simulation results show that the error resilience performance is greatly improved,and the PSNR gains from 0.5 to 7dB can be achieved under different channel conditions.
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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 | 0.002 | 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