The effect of crosstalk on perceived depth in 3D displays
Bibliographic record
Abstract
Crosstalk in stereoscopic displays is defined as the leakage of one eye's image into the image of the other eye. All popular commercial stereoscopic viewing systems, including the ones used in movie theaters, suffer from crosstalk to some extent. It has been shown that crosstalk causes image distortions and reduces image quality. Moreover, it decreases visual comfort and affects one's ability to discriminate object shape and judge the relative depth of two objects. These results have potentially important implications for the quality and the accuracy of depth percepts in 3d display systems. To asses this hypothesis directly, we have explored the effect of crosstalk on the perceived magnitude of depth in a variety of stereoscopic stimuli. We found that with simple synthetic images increasing crosstalk beyond four percent resulted in a significant decrease in the magnitude of perceived depth, especially for larger disparities. This degradation was largely independent of the spatial separation of the ghost image. Further, we found qualitatively and quantitatively similar detrimental effects of crosstalk on perceived depth in complex images of natural scenes. The consistency of the negative impact of crosstalk, regardless of image complexity, suggests that it is not ameliorated by the presence of pictorial depth cues. We have recommended that display manufacturers keep crosstalk levels below the critical value of four percent to achieve optimal depth quality. Meeting abstract presented at OSA Fall Vision 2012
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".