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Record W2038764041 · doi:10.1117/12.510787

Stereoscopic video telephony: naturalness and sense of presence

2003· article· en· W2038764041 on OpenAlexaff
Wa James Tam, Ronald Renaud, A. Vincent, Taali Martin, Phil Blanchfield

Bibliographic record

VenueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2003
Typearticle
Languageen
FieldComputer Science
TopicImage and Video Quality Assessment
Canadian institutionsCommunications Research Centre Canada
Fundersnot available
KeywordsNaturalnessStereoscopyComputer scienceComputer visionArtificial intelligencePixelBrightnessComputer graphics (images)OpticsPhysics

Abstract

fetched live from OpenAlex

Previous research from our laboratory indicated that sense of presence is enhanced for stereoscopic images with scenes typical of video telephone (VT) systems, compared to non-stereoscopic 2-D images. However, this enhancement was not found for all sequences. In the present study we report results obtained with a new set of stereoscopic sequences containing depth scenes that were created through manipulation of camera focal length, background scene, and camera convergence point. Viewers were asked to rate both stereoscopic and non-stereoscopic versions of the sequences on naturalness and sense of presence. The methodology of double-stimulus, continuous quality scale (ITU-R Recommendation 500) was used in the subjective assessment. Images in the video sequences were common image format (CIF, 352 x 240 pixels) with a display size of 15.5 cm x 11.6 cm. The results confirmed our previous findings that sense of presence is enhanced for certain stereoscopic video sequences, compared to non-stereoscopic sequences. The results also indicate a high correlation between ratings of naturalness and sense of presence (r<sup>2</sup> = 0.75), although ratings tended to be lower for naturalness than for presence. Both ratings tended to improve slightly with camera focal length, except for sequences with a natural background. For the range studied, no effect of camera convergence point was found.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.710
Threshold uncertainty score0.859

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.013
GPT teacher head0.252
Teacher spread0.239 · 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 designBench or experimental
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

Citations3
Published2003
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIESame topicImage and Video Quality AssessmentFrench-language works237,207