Assessment of Technical Quality of Online Video Using Visualization in Place of Experience
Bibliographic record
Abstract
This paper introduces a new method to collect subjective ratings of Technical Quality (TQ) in disrupted video (DV). TQ is related to whether or not the video has disruptions such as impairments (re-buffering, perceived as freezing for a period of time followed by resumption of video playback) or failures (where the video playback stops part way through and fails to complete). The assessment method introduced in this paper avoids the confounding effects of content on TQ ratings and reduces the time and effort necessary to run experiments. Actual videos, as stimuli, are replaced with schematic representations of those videos. We ran an experiment with 37 participants to explore the viability of assessing TQ using visualizations instead of actual videos. The experiment had contrasting conditions that compared TQ ratings of actual videos, vs. schematic representations of videos. The results of the experiment showed that, with appropriate training, ratings of TQ made after viewing the visualizations only were similar to TQ ratings made after actually watching videos with corresponding impairments or failures.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".