<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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".