<title>Viewing stereoscopic images comfortably: the effects of whole-field vertical disparity</title>
Bibliographic record
Abstract
Stereoscopic images while providing enhanced depth and image quality can cause moderate discomfort. In this paper, we present the results of two experiments aimed at investigating one possible source of discomfort: whole-field vertical disparities. In both experiments, we asked viewers to rate their comfort level while viewing a 3D feature film in which the left and right images were vertically misaligned. The feature film was presented on a large theater type screen. In Experiment 1, the vertical offset was changed randomly on a scene-by-scene basis resulting in an average vertical disparity of 31 minutes or arc at the closest viewing distance. The results showed that whole- field vertical disparities produced a marginal increase in discomfort that became only slightly more pronounced with time. In Experiment 2, we alternated periods of low, medium and high levels of whole-field vertical disparity. At the closest distance, the mean vertical disparity was 15, 30, or 62 minutes of arc for the low, medium and high disparity conditions, respectively. In this experiment, discomfort increased with vertical disparity, but again only marginally even after prolonged exposure. We conclude that whole-field vertical disparities cannot be a major contributor to the discomfort experienced by observers when viewing stereoscopic images.
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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 | 0.000 | 0.001 |
| 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) | 0.010 | 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".