Quality-of-Experience for Adaptive Streaming Videos: An Expectation Confirmation Theory Motivated Approach
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
The dynamic adaptive streaming over HTTP (DASH) provides an inter-operable solution to overcome volatile network conditions, but how the human visual quality-ofexperience (QoE) changes with time-varying video quality is not well-understood. Here, we build a large-scale video database of time-varying quality and design a series of subjective experiments to investigate how humans respond to compression level, spatial and temporal resolution adaptations. Our path-analytic results show that quality adaptations influence the QoE by modifying the perceived quality of subsequent video segments. Specifically, the quality deviation introduced by quality adaptations is asymmetric with respect to the adaptation direction, which is further influenced by other factors such as compression level and content. Furthermore, we propose an objective QoE model by integrating the empirical findings from our subjective experiments and the expectation confirmation theory (ECT). Experimental results show that the proposed ECT-QoE model is in close agreement with subjective opinions and significantly outperforms existing QoE models. The video database together with the code are available online at https://ece.uwaterloo.ca/~zduanmu/tip2018ectqoe/.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| 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