Generating the Depth Map from the Motion Information of H.264-Encoded 2D Video Sequence
Bibliographic record
Abstract
An efficient method that estimates the depth map of a 3D-scene using the motion information of the H.264-encoded 2D-video is presented. The motion information of the video-frames captured via a single camera is either directly used or modified to approximate the displacement (disparity) that exists between the right and left images when the scene is captured by stereoscopic cameras. Then, depth is estimated based on its inverse relation with disparity. The low-complexity of this method and its compatibility with future broadcasting networks allow its real-time implementation at the receiver; thus 3D-signal is constructed at no additional burden to the network. Performance evaluations show that this method outperforms the other existing H.264-based technique by up to 1.98 dB PSNR, providing more realistic depth information of the scene. Moreover subjective comparisons of the results, obtained by viewers watching the generated stereo video sequences on a 3D-display system, confirm the superiority 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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".