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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 source (direct Gemma or distilled Codex), 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".