Quality-of-Experience of Adaptive Video Streaming
Why this work is in the frame
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Bibliographic record
Abstract
With the remarkable growth of adaptive streaming media applications, especially the wide usage of dynamic adaptive streaming schemes over HTTP (DASH), it becomes ever more important to understand the perceptual quality-of-experience (QoE) of end users, who may be constantly experiencing adaptations (switchings) of video bitrate, spatial resolution, and frame-rate from one time segment to another in a scale of a few seconds. This is a sophisticated and challenging problem, for which existing visual studies provide very limited guidance. Here we build a new adaptive streaming video database and carry out a series of subjective experiments to understand human QoE behaviors in this multi-dimensional adaptation space. Our study leads to several useful findings. First, our path-analytic results show that quality deviation introduced by quality adaptation is asymmetric with respect to the adaptation direction (positive or negative), and is further influenced by the intensity of quality change (intensity), dimension of adaptation (type), intrinsic video quality (level), content, and the interactions between them. Second, we find that for the same intensity of quality adaptation, a positive adaptation occurred in the low-quality range has more impact on QoE, suggesting an interesting Weber's law effect; while such phenomenon is reversed for a negative adaptation. Third, existing objective video quality assessment models are very limited in predicting time-varying video quality.
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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 | 0.000 | 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 | 0.001 | 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