Efficient Transmission of Multiple Broadcasting Services Using LDM and SHVC
Bibliographic record
Abstract
In this paper, we present an efficient transmission method of multiple broadcasting services using layered division multiplexing (LDM) and scalable high efficiency video coding (SHVC) based on the next generation terrestrial digital broadcasting standard, advanced television systems committee 3.0. In a two-layer LDM system, each layer forms a physical layer pipe that carries base layer and enhancement layer video streams of SHVC, respectively. This combination of physical and presentation layers technologies can maximize channel utilization when multiple broadcasting services are delivered in a single radio frequency channel with different robustness and reception conditions. An intensive performance analysis is provided when the proposed combination of LDM and SHVC is compared with other approaches, such as time division multiplexing and/or high efficiency video coding simulcast broadcasting. Several performance measurements are also introduced, such as constant data rate measurement and constant quality measurement, which are used to evaluate the performance of video codecs in conjunction with different physical layer system parameters. A prototype hardware system with LDM and SHVC capable of selecting different parameter combinations is tested in the laboratory and in real field environments to verify the performance and feasibility of the proposed LDM and SHVC combination. Results show that the proposed LDM and SHVC combination provides significant gains on video service quality as well as reception robustness.
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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 | 0.000 |
| 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".