Depth of Field Image Sequences: 3D Cuing of High Efficiency
Bibliographic record
Abstract
We study the ensemble of depth of field (DOF) images pertaining to continuously varying focal distance but with the position, angle and aperture of the camera fixed, called the DOF image sequence. It is shown that all member images of the ensemble can be approximated with good precision as a linear combination of few basis images. By exploiting the above newly discovered sparsity structure of DOF images, we develop a new coding scheme for DOF image sequences. The encoder works as a DOF image modeler; reciprocally the decoder acts as an ultra fast DOF image renderer. This coding scheme enables real-time generation of DOF videos that achieve realistic 3D perceptions via combined use of motion parallax and depth of field. The proposed new technique outperforms the image-based DOF rendering in image quality while having a lower complexity. The same sparsity of DOF images also inspires our design of a new computational display system that can offer multiview DOF video presentations to different users all on a common screen. Experimental results show practical values of this research in multiuser VR applications, in both aspects of content generation and presentation.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".