Smoothing depth maps for improved steroscopic image quality
Bibliographic record
Abstract
A technique to improve the image quality of stereoscopic pictures generated from depth maps (depth image based rendering or DIBR) is examined. In general, there are two fundamental problems with DIBR: a depth map could contain artifacts (e.g., noise or "blockiness") and there is no explicit information on how to render newly exposed regions ("holes") in the rendered image as a result of new virtual camera positions. We hypothesized that smoothing depth maps before rendering will not only minimize the effects of noise and distortions in the depth maps but will also reduce areas of newly exposed regions where potential artifacts can arise. A formal subjective assessment of four stereoscopic sequences of natural scenes was conducted with 23 viewers. The stereoscopic sequences consisted of source images for the left-eye view and rendered images for the right-eye view. The depth maps were smoothed with a Gaussian blur filter at different levels of strength before depth image based rendering. Results indicated that ratings of perceived image quality improved with increasing levels of smoothing of the depth maps. Even though the depth maps were smoothed, a negative effect on ratings of overall perceived depth quality was not 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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".