Bibliographic record
Abstract
There has been an increasing demand for interactive video transmission over the Internet for applications such as video conferencing, video calls, and telepresence applications. These applications are increasingly moving towards providing High Definition (HD) video quality to users. A key challenge in these applications is to preserve the quality of video when it is transported over best-effort networks that do not guarantee lossless transport of video packets. In such conditions, it is important to protect the transmitted video by using intelligent and adaptive protection schemes. Applications such as HD video conferencing require live interaction among participants, which limits the overall delay the system can tolerate. Therefore, the protection scheme should add little or no extra delay to video transport. We propose a novel Adaptive Loss Protection (ALP) scheme for interactive HD video applications such as video conferencing and video chats. This scheme adds negligible delay to the transmission process and is shown to achieve better quality than other schemes in lossy networks. The proposed ALP scheme adaptively applies four different protection modes to cope with the dynamic network conditions, which results in high video quality in all network conditions. Our ALP scheme consists of four protection modes ; each of these modes utilizes a protection method . Two of the modes rely on the state-of-the-art protection methods, and we propose a new Integrated Loss Protection (ILP) method for the other two modes. In the ILP method we integrate three factors for distributing the protection among packets. These three factors are error propagation, region of interest and header information. In order to decide when to switch between the protection modes, a new metric is proposed based on the effectiveness of each mode in performing protection, rather than just considering network statistics such as packet loss rate. Results show that by using this metric not only the overall quality will be improved but also the variance of quality will decrease. One of the main advantages of the proposed ALP scheme is that it does not increase the bit rate overhead in poor network conditions. Our results show a significant gain in video quality, up to 3dB PSNR improvement is achieved using our scheme, compared to protecting all packets equally with the same amount of overhead.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.379 | 0.234 |
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".