On reducing the size of structured meshes with a novel video object extraction algorithm
Bibliographic record
Abstract
Mesh-based motion analysis is a popular inter-frame video compression model. Compression with this model is further increased when the size of the mesh used to represent video frames is reduced. In this paper, a video object extraction algorithm that reduces the size of structured meshes is proposed. It uses block-matching analysis to extract video objects (or dynamic contents) in video frames, and subsequently reduces the mesh size by removing the nodes that represent the video background (or motionless contents). In essence, only video objects are represented in the reduced mesh. During frame reconstruction, video objects are recovered from the affine transform, while the video background is mapped directly from the reference frame. Our study shows that the proposed algorithm can reduce on average 77.9% of the mesh nodes per frame for the Claire sequence (which has a motionless background). Also, the frame reconstruction quality is not affected as a result of using the proposed algorithm. Therefore, we claim that the proposed algorithm is effective in achieving high video compression.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".