Stereo image quality: effects of mixed spatio-temporal resolution
Bibliographic record
Abstract
We explored the response of the human visual system to mixed-resolution stereo video-sequences, in which one eye view was spatially or temporally low-pass filtered. It was expected that the perceived quality, depth, and sharpness would be relatively unaffected by low-pass filtering, compared to the case where both eyes viewed a filtered image. Subjects viewed two 10-second stereo video-sequences, in which the right-eye frames were filtered vertically (V) and horizontally (H) at 1/2 H, 1/2 V, 1/4 H, 1/4 V, 1/2 H 1/2 V, 1/2 H 1/4 V, 1/4 H 1/2 V, and 1/4 H 1/4 V resolution. Temporal filtering was implemented for a subset of these conditions at 1/2 temporal resolution, or with drop-and-repeat frames. Subjects rated the overall quality, sharpness, and overall sensation of depth. It was found that spatial filtering produced acceptable results: the overall sensation of depth was unaffected by low-pass filtering, while ratings of quality and of sharpness were strongly weighted towards the eye with the greater spatial resolution. By comparison, temporal filtering produced unacceptable results: field averaging and drop-and-repeat frame conditions yielded images with poor quality and sharpness, even though perceived depth was relatively unaffected. We conclude that spatial filtering of one channel of a stereo video-sequence may be an effective means of reducing the transmission bandwidth.
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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 | 0.005 |
| 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".