Distortion optimization in enriched video traces for End-to-End video quality enhancement
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Video compression and streaming over lossy wireless networks is the current trend in telecommunication and encouraged by major carriers to increase their services portfolio. However, the technological challenges are still tackled to enable the delivery of high quality video. In our previous work, we developed a per stream distortion model that is based on the extended Gilbert model. We expanded our definition of the verbose video trace file with embedded coefficients derived from our distortion model. These coefficients are video quality descriptors that we associate with Quality of Service parameters that are measured in real-simulation time. In this work, we devise an optimization algorithm that is based on the distortion model to enhance the received video quality in End-to-End (E2E) communications. Our optimization algorithm is tested in multiple simulations to verify its validity. We report the results from our simulations that indicate significant improvements when this optimization algorithm is deployed in Edge Routers in conjunction with our video stream distortion coefficients and network metrics.
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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 | 0.001 | 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 | 0.001 | 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 it