Motion-Compensated Frame Prediction with Global Motion Estimation for Image Sequence Compression
Bibliographic record
Abstract
Based on a perspective projection model of six-parameter camera motions, a novel motion-compensated frame prediction approach is presented, taking into consideration all types of camera motions including camera translations. With the assumption of a continuously changing scene-depth distribution, a block-based scaled-depth estimation technique is proposed for obtaining the scaled-depth map of the predicted frame. The motion-compensated predicting frame is generated by using the global camera rotation and translation parameters combined with the scaled block-depth map. Compared with traditional block matching algorithms (BMA), more accurate predicting frames are obtained with the proposed motion-compensated frame prediction approach. As a result of fewer parameters required for motion compensation and reduced prediction residues, this approach is potentially more efficient if applied in frame prediction for image sequence compression. Experimental results on test images demonstrate the effectiveness of the proposed approach, and also demonstrate its superior performance over the traditional BMA applied for motion-compensated frame prediction.
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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 | 0.000 | 0.000 |
| 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 | 0.002 |
| Open science | 0.000 | 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".