Study of Rating Scales for Subjective Quality Assessment of High-Definition Video
Bibliographic record
Abstract
With the constant evolution of video technology and the deployment of new video services, content providers and broadcasters always face the challenge of delivering an adequate video quality which meets end-users expectations. The development of reliable quality testing and quality monitoring tools that can be used by broadcasters ultimately requires reliable objective video quality metrics. In turn, the validation of these objective models requires reliable subjective assessment, the most accurate representation of the quality perceived by end-users. Many different subjective assessment methodologies exist, and each has its advantages and drawbacks. One important element in a subjective testing methodology is the choice of the rating scale. In this paper, we make a direct comparison between four scales, which are either included in existing international standards or proposed to be used in future standardization activities related to video quality. We examine the subjective data from the points of view of response behavior from participants, similarity and variability of subjective scores. We discuss these results within the context of the subjective quality assessment of high-definition video compressed and transmitted over error-prone networks. Our experimental data show no overall statistical differences between the different scales. Results also show that the single-stimulus presentation provides highly repeatable results even if different scales or groups of participants are used.
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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 | 0.048 | 0.183 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".