Modeling DV/DVCPRO Standards on Reconfigurable Video Coding Framework
Bibliographic record
Abstract
After more than 20 years, several video coding standards and technologies have been delivered. Less consideration is taken on their commonalities and interoperations. Specification and reference code of case by case is time consuming. The MPEG reconfigurable video coding (RVC) framework is a new standard under development by MPEG. It aims to provide a unified high‐level specification of current MPEG video coding technologies. In this framework, the decoder is built as a configuration of video coding tools taken from MPEG toolbox library. Up to now, MPEG‐4 simple profile and China audio video coding standard (AVS) decoders have been successfully modeled with RVC framework. In this paper, we examine another video standard, that is, DV/DVCPRO, and model it with RVC‐CAL. The flexibility and ease of RVC‐CAL is demonstrated as well as the validation of RVC modeling.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".