Stereoscopic video telephony: naturalness and sense of presence
Bibliographic record
Abstract
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 (r2 = 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.
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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 | 0.001 | 0.003 |
| 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 | 0.000 |
| 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".