A Quality-of-Experience Index for Streaming Video
Bibliographic record
Abstract
With the rapid growth of streaming media applications, there has been a strong demand of quality-of-experience (QoE) measurement and QoE-driven video delivery technologies. Most existing methods rely on bitrate and global statistics of stalling events for QoE prediction. This is problematic for two reasons. First, using the same bitrate to encode different video content results in drastically different presentation quality. Second, the interactions between video presentation quality and playback stalling experiences are not accounted for. In this work, we first build a streaming video database and carry out a subjective user study to investigate the human responses to the combined effect of video compression, initial buffering, and stalling. We then propose a novel QoE prediction approach named Streaming QoE Index that accounts for the instantaneous quality degradation due to perceptual video presentation impairment, the playback stalling events, and the instantaneous interactions between them. Experimental results show that the proposed model is in close agreement with subjective opinions and significantly outperforms existing QoE models. The proposed model provides a highly effective and efficient meanings for QoE prediction in video streaming services.1
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".