Object-oriented coding using successive motion field segmentation and estimation
Why this work is in the frame
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Bibliographic record
Abstract
Block-based motion compensation assumes that all pixels within a block have the same translational motion. That hypothesis, however, results in inaccurate compensation of moving objects' boundaries. Object-oriented video compression algorithms typically segment each image in regions of uniform motion and estimates the motion of these regions to generate more accurate motion compensated images. We present a two-stage algorithm for motion field segmentation and estimation in an object-oriented coder. In the algorithm's first stage, a standard block-matching algorithm and a maximum a posteriori probability estimate are used to compute a translational motion field and its segmentation. This segmentation is then utilized in the second stage to estimate the parameters of complex motion models. The parameters of the complex motion models are only estimated in the algorithm's second stage which reduces the computational complexity of the proposed algorithm. Simulation results show that the proposed algorithm significantly reduces the bit rate needed to encode video sequences when compared to standard block-based algorithms.
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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 | 0.001 | 0.005 |
| 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 it