Depth image based rendering for multiview stereoscopic displays: role of information at object boundaries
Bibliographic record
Abstract
Depth image based rendering (DIBR) is useful for multiview autostereoscopic systems because it can produce a set of new images with different camera viewpoints, based on a single two-dimensional (2D) image and its corresponding depth map. In this study we investigated the role of object boundaries in depth maps for DIBR. Using a standard subjective assessment method, we asked viewers to evaluate the depth and the image quality of stereoscopic images in which the view for the right eye was rendered using (a) full depth maps, (b) partial depth maps containing full depth information but that was only located at object boundaries and edges, and (c) partial depth maps containing binary depth information at object boundaries and edges. Results indicate that depth quality was enhanced and image quality was slightly reduced for all test conditions, compared to a reference condition consisting of 2D images. The present results confirm previous observations indicating that depth information at object boundaries is sufficient in DIBR to create new views such as to produce a stereoscopic effect. However, depth ratings for the partial depth maps tended to be slightly lower than those generated with the full depth maps. The present study also indicates that more research is needed to increase the depth and image quality of the rendered stereoscopic images based on DIBR before the technique can be of wide and practical use.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| 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 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".