Evaluating packet erasure recovery techniques for video streaming
Bibliographic record
Abstract
In real time video transmission over Internet Protocol (IP), to make the video stream more resilient against packet loss in the network, packet erasure coding (EC) is applied as one form of packet loss concealment. The separation of source and channel coding even though sub optimum, offers the convenience of simplified implementations. In the framework of separate video and channel coding, significant benefits can be derived from understanding how to jointly adjust the video and packet erasure coding parameters to improve the quality of the reconstructed media after packet erasure recovery. To this end, in this paper, a testbed is presented for the evaluation of a real time encoding of interactive video applications with packet erasure coding at the IP layer. Specifically, practical video CODECs like H.264/AVC with different packetization strategies are deployed to test the improvements obtained in the recovered video stream for different coding rates in packet erasure codes. Quality of video is analyzed using PSNR of reconstructed video frames under different raw packet loss rates (PLRs) and for different packet sizes.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".