Stereoscopic image rendering based on depth maps created from blur and edge information
Bibliographic record
Abstract
Depth image based rendering (DIBR) is suited for 3D-TV and for autostereoscopic multiview displays. With DIBR, each 2D image captured with a camera at a given position has an associated depth map. This map is used to process the original 2D image so as to generate new images as if they were taken from different camera viewpoints. In the present study we examined the depth and image quality of stereoscopic 3D images that were generated using surrogate depth maps, that is, maps that were created using blur and edge information from the original 2D images. Depth maps were created with three different methods. Formal subjective assessments indicated that the stereoscopic images thus created have enhanced depth quality, with a marginal loss in image quality, when compared to the original non-stereoscopic images. This finding of enhanced depth is surprising because the surrogate depth maps contained limited depth information and mainly at object boundaries. We speculate that the visual system combines the information from pictorial depth cues and from depth interpolation between object boundaries and edges to arrive at an overall perception of depth. The methods for creating the depth maps for stereoscopic imaging that were investigated in this study might be used in applications where depth accuracy is not critical.
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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 | 0.000 | 0.002 |
| 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) | 0.002 | 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".