Nonuniform smoothing of depth maps before image-based rendering
Bibliographic record
Abstract
In a technique called depth image based rendering, new images are generated using information from an original source image and its corresponding depth map, such that the new images appear to have been taken from different camera viewpoints. This technique is bandwidth-efficient and is ideal for multiview display systems, such as autostereoscopic 3D-TV. In a previous study, we demonstrated that uniform smoothing of depth maps through Gaussian filtering helps improve the image quality of the rendered images. In the present study we investigated the potential benefits of two non-uniform smoothing methods: asymmetric smoothing, where the horizontal extent of smoothing was smaller than that in the vertical direction, and adaptive smoothing, where the level and extent of smoothing was based on the local depth magnitude. In this vein, ten viewers assessed image quality and depth quality of four stereoscopic images in which the view to one eye was a rendered image based on one of the three smoothing methods: uniform, asymmetric, or adaptive. The experimental results showed an improvement in ratings of image quality for all three methods as the level of smoothing was increased. The results also indicated a slight advantage in image quality for asymmetric smoothing over the other two methods. Ratings of overall depth quality were significantly higher than corresponding non-stereoscopic references for all three methods, although the ratings decreased at the highest level of smoothing that was used in the present study. In general, ratings of depth quality tended to be marginally lower for the asymmetric method.
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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) | 0.004 | 0.001 |
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".