Temporal sub-sampling of depth maps in depth image-based rendering of stereoscopic image sequences
Bibliographic record
Abstract
In depth image based rendering, video sequences and their associated depth maps are used to render new camera viewpoints for stereoscopic applications. In this study, we examined the effect of temporal downsampling of the depth maps on stereoscopic depth quality and visual comfort. The depth maps of four eight-second video sequences were temporally downsampled by dropping all frames, except the first, for every 2, 4, or 8 consecutive frames. The dropped frames were then replaced by the retained frame. Test stereoscopic sequences were generated by using the original image sequences for the left-eye view and the rendered image sequences for the right-eye view. The downsampled versions were compared to a reference version with full depth maps that were not downsampled. Based on the data from 21 viewers, ratings of depth quality for the downsampled versions were lower. Importantly, ratings depended on the content characteristics of the stereoscopic video sequences. Results were similar for visual comfort, except that the differences in ratings between sequences were larger. The present results suggest that more processing, such as interpolation of depth maps, might be required to counter the negative effects of temporal downsampling, especially beyond a downsampling of two.
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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 | 0.006 |
| 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 | 0.001 |
| 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".