A Possible Predictor of Visual Discomfort of Viewing Stereoscopic 3D Maps: The Imbalance of Disparity Distributions
Bibliographic record
Abstract
Stereoscopic 3D (S3D) maps provide an accurate 3D representation of terrain texture for the precise perception of Earth’s surface. Visual discomfort on S3D images primarily comes from accommodation-vergence conflict, which is related to disparity (the distance between two corresponding points in the left and right stereo images). Previous studies have identified that disparity characteristics are related to visual discomfort. However, the relation between disparity characteristics and visual discomfort has not been investigated in orthographic S3D maps. It is unknown whether disparity characteristics are good indicators of visual discomfort regarding S3D maps. This study proposed a new visual discomfort predictor and compared it to the disparity characteristics already existing in the IEEE standard 3333.1.1™-2015. The comparisons indicate that the imbalance index can be a good predictor of visual discomfort regarding S3D maps. The predictor will be used in a personalized computational model to predict visual discomfort.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".