Improving the visual comfort of stereoscopic images
Bibliographic record
Abstract
We compared the visual comfort and apparent depth of stereoscopic images for three camera configurations: parallel (without image shift), image-shifted and converged. In the parallel and image-shifted configurations, the stereo cameras were pointed straight ahead. In the converged configuration the cameras were toed-in. In the image-shifted configuration the image frame was shifted perpendicularly with respect to the line of sight of the camera. The parallel configuration produces images with uncomfortably large disparities for objects near the camera. By converging the cameras or by shifting the image, these large disparities can be reduced and visual comfort can be improved. However, the converged configuration introduces keystone distortions into the image, which can produce visual discomfort. The image-shifted configuration does not introduce keystone distortions, but affects the width of the image frame. It also requires unusual camera hardware or computer post-processing to shift the images. We found that converged and image-shifted configurations improved the visual comfort of stereoscopic images by an equivalent amount, without affecting the apparent depth. Keystone distortions in the converged configuration had no appreciable negative effect on visual comfort.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".