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Record W2030925510 · doi:10.1117/12.474108

Comparison of stereoscopic and nonstereoscopic video images for visual telephone systems

2003· article· en· W2030925510 on OpenAlexaff
Wa James Tam, A. Vincent, Ronald Renaud, Phil Blanchfield, Taali Martin

Bibliographic record

VenueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2003
Typearticle
Languageen
FieldComputer Science
TopicVirtual Reality Applications and Impacts
Canadian institutionsCommunications Research Centre Canada
Fundersnot available
KeywordsStereoscopyComputer scienceComputer visionCodecArtificial intelligenceImage qualityPerceptionHuman visual system modelPixelComputer graphics (images)Image (mathematics)Telecommunications

Abstract

fetched live from OpenAlex

Possible differences in perceptual qualities between stereoscopic and non-stereoscopic images were investigated using a field-sequential stereoscopic display. A total of forty non-expert viewers were asked to rate overall image quality, sharpness and sense of presence using the double-stimulus continuous quality scale (ITU-R Recommendation 500). Viewers rated a set of five video sequences (stereoscopic and non-stereoscopic) each presented at four levels of image quality that were obtained by varying the quantization level (Q=0, 32, 36, and 39) of a generic H.264 video compression codec. Each sequence was 8 seconds long at 30 frames per second and the spatial resolution of each frame was common image format (CIF, 352 x 240 pixels). Image size was 15.5 cm x 11.6 cm and scene contents were representative of visual telephone systems with one, two or three individuals. The experimental results showed that viewers' ratings depended on the sequences and that there was no reliable difference between stereoscopic and non-stereoscopic sequences in terms of image quality and perceived sharpness. However, binocular disparity tended to improve ratings of sense of presence. We conclude that incorporating stereoscopic information into visual telephone systems can be useful for enhancing sense of presence.

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 score0.698
Threshold uncertainty score0.835

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.019
GPT teacher head0.284
Teacher spread0.265 · 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

Citations1
Published2003
Admission routes1
Has abstractyes

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Same venueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIESame topicVirtual Reality Applications and ImpactsFrench-language works237,207