Real-time DVB-MHP to Blu-ray system information transcoding
Bibliographic record
Abstract
The demand for interactive TV services is rapidly increasing. Most of the interactive TV systems are based on the DVB-MHP (digital video broadcast - multimedia home platform) format. Given the unprecedented success of DVD technology and its already established installed base, it is definitely in the interest of manufactures and end-users to be able to play back DVB-MHP iTV content using a Blu-ray player. This study addresses how such programs could be played by the Blu-ray system in real-time. One of the main challenges in realizing this compatibility is the conversion of the "system information " data. These data carry the information about the broadcasting programs and services. This paper first analyzes the differences in system information between the two standards, DVB-MHP and Blu-ray; mainly the information each standard requires, where this information is stored, and how it is organized. We then propose methods to transcode this information in real-time. To derive the data required to generate the Blu-ray system information from a transport stream, we propose a data retrieval scheme that avoids fully demultiplexing a transport stream. To extract the needed information from the DVB-MHP video stream, we propose an efficient search algorithm. Finally, an improved structure that transcodes the incoming system information is developed. This transcoder only transcodes the updated versions of the system information and at the same time keeps the rate at which this information is transmitted to the Blu-ray system the same as that in DVB-MHP.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".