Quality of Service Architectures for Wireless Networks
Bibliographic record
Abstract
This chapter describes several advanced techniques for estimating the video distortion deriving from multiple video packet losses. It provides different usage scenarios, where the Peak Signal to Noise Ratio (PSNR) video metric may be used for improving the end user quality. The key idea of the presented applications is to effectively use the distortion information associated to each video packet. This allows one to perform optimal decisions in the selection of the more suitable packets to transmit. During the encoding process, the encoder estimates first the loss impact (for instance the amount of error propagation) of each packet. Afterwards, it generates side information as a “hint” for making video content aware transmission decisions. In this way, it is possible to define new scheduling schemes that give more priority to the packets with higher loss impact, and to assign fewer resources to the packets with lower loss impact. To this end, the usage of hint tracks, introduced in the MPEG-4 systems part, provides a syntactic means for storing scheduling information about media packets that significantly simplifies the operations of a streaming server. Moreover, the prioritization scheme may be used to minimize the overall error propagation under the delay constraint imposed by the video presentation deadline. The chapter also reviews recent research advances in the field of QoS mechanisms that adopt video specific metrics to improve the end user perceived quality.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.013 |
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".