A new object-oriented approach for video compression at very low bit rate
Bibliographic record
Abstract
Object-oriented approaches have been proposed for coding video sequences at very low bit rate. Typically, object-oriented coding algorithms segment each image into regions of uniform motion and estimate motion of these regions to generate more accurate motion compensated images. Due to the iterative computing of complex motion models' parameters, the computational complexity of object-oriented algorithms is often high. The present author gives a two-stage algorithm for motion field segmentation and estimation in an object-oriented coder whose computational complexity is reduced by delaying the use of complex motion models at the end of the proposed algorithm. In the first stage of the algorithm, a standard block-matching algorithm and a maximum a posteriori probability estimate are used to compute a translational motion field and its segmentation. That segmentation is then utilized in the second stage to estimate the parameters of complex motion models. Compared to standard block-based algorithms, simulation results show that the proposed algorithm significantly reduces the bit rate needed to encode video sequences and is appropriate for very low bit rate applications.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".