Estimation of time varying QoE for high definition IPTV distribution
Bibliographic record
Abstract
High Definition (HD) content delivery over IP networks is now a reality in the entertainment marketplace. Such networks can suffer packet loss, especially on the last mile link. This results in time-variant video quality. In this paper we first present an analysis of the human perception of network impairment on HD IPTV quality. Second, we propose a method, based on common objective measurements, to estimate subjective judgments of time-variant quality. A testbed was deployed to emulate a real use case of delivering high definition TV material over an IPTV network. The quality of the delivered H.264/AVC encoded video was evaluated objectively in an emulated environment where packet loss impairments were generated. Objective measurements were then compared to continuous subjective quality ratings. Based on the obtained results, highly correlated models were developed for the estimation of instantaneous user judgment of impaired HD broadcast IPTV.
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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 | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 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".