<title>Subjective quality assessment and the effect of context in expert and nonexpert viewers</title>
Bibliographic record
Abstract
The perceived quality of video sequences is generally measured using standard subjective methods. It has been argued that these methods, which typically consist of scaling judgment tasks, are affected by context effects. Context effects are observed when the perceived quality of a video sequence is influenced by the perceived quality of the other video sequences included in the test. Several studies have confirmed the presence of context effects. However, the same studies are ambiguous with respect to the issue of which methods are affected the most. In addition, context effects have been investigated mainly with non-expert viewers. In this study, we investigated context effects in both expert and non-expert viewers. Two experiments were conducted to investigate the relationships between context effects, level of expertise, and type of subjective method. In Experiment 1, we measured range and frequency context effects for two different subjective assessment methods, a double stimulus method (i.e., DSCQS) and a comparison scaling method, using non-expert viewers. We found no frequency context effect with both methods, and a marginal range context effect with the DSCQS method. In Experiment 2, we obtained the same measurements with expert viewers. We found no context frequency effect for both subjective methods, and a very small range context effect for the comparison method only.
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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 | 0.003 | 0.033 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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".