An H.264-based scheme for 2D to 3D video conversion
Bibliographic record
Abstract
An efficient method that converts 2D video sequences to 3D is presented. This method utilizes the motion information between consecutive frames to approximate the depth map of the scene. To estimate the depth map, the horizontal motion captured by a single camera is revised and then approximated as the displacement between the right and left frames captured by two cameras in a stereoscopic set-up case. To enhance the visual depth perception, a non-linear scaling model is then applied to the modified motion vectors. The low complexity of our approach and its compatibility with future 3D systems, allows real-time implementations at the receiver-end for no additional burden on the network. Performance evaluations show that our approach outperforms the existing H.264-based depth map estimation technique by 1.84 dB PSNR, providing more realistic depth representation of the scene. Moreover, the subjective comparison of results (obtained by viewers watching the generated stereo video sequences on a 3D display system) confirms the better performance of our 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".