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Record W1973353870 · doi:10.1167/12.14.4

The effect of crosstalk on perceived depth in 3D displays

2012· article· en· W1973353870 on OpenAlexaff
Inna Tsirlin, Laurie M. Wilcox, Robert S. Allison

Bibliographic record

VenueJournal of Vision · 2012
Typearticle
Languageen
FieldEngineering
TopicAdvanced Optical Imaging Technologies
Canadian institutionsYork University
Fundersnot available
KeywordsCrosstalkStereoscopyImage qualityStereo displayComputer scienceArtificial intelligenceBinocular disparityDepth perceptionComputer visionPerceptionOpticsPsychologyPhysicsImage (mathematics)

Abstract

fetched live from OpenAlex

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

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.822
Threshold uncertainty score0.154

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.006
GPT teacher head0.285
Teacher spread0.279 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2012
Admission routes1
Has abstractyes

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