Quality-of-experience prediction for streaming video
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
With the rapid growth of streaming media applications, there has been a strong demand of objective models that can predict end users' quality-of-experience (QoE) when watching the video being streamed to their display devices. Existing methods typically use bitrate and global statistics of stalling events as the QoE indicators. This is problematic for two reasons. First, using the same bitrate to encode different video content could result in drastically different presentation QoE. Second, the interactions between presentation visual quality and playback stalling are not accounted for. Here we propose a novel QoE prediction approach that takes into consideration the instantaneous quality degradation due to perceptual video presentation impairment, the playback stalling events caused by imperfect network delivery, and the instantaneous interactions between presentation quality and playback stalling. The proposed algorithm demonstrates strong promise when tested using a subject-rated video streaming QoE database.
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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 | 0.000 | 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 it