Modelling prioritized MPEG video using TES and a frame spreading strategy for transmission in ATM networks
Bibliographic record
Abstract
This paper presents an efficient transmission mechanism, using frame spreading, for variable bitrate (VBR) MPEG compressed video, through an ATM multiplexer, such as a cable head-end. A priority scheme is implemented in a software MPEG encoder which produces a proportionate traffic in both (i.e., high and low) priority partitions for all three frame types (intraframe, predicted and interpolated) used in MPEG. An ATM multiplexer with a pushout buffer scheme is implemented for the study, in order to provide priority scheduling at the multiplexer for the two priority partitions. The multiplexer is fed with VBR MPEG traffic and performance statistics such as the cell loss ratios are studied for various frame spreading scenarios. Two statistical models are developed using TES (transform expand sample) for VBR MPEG video having two levels of priority. The first model is matched with the empirical histogram and autocorrelation function of each frame type (I, P and B). The second model is created with the assumption of a gamma distribution for the number of bits in each frame type. Experiments are conducted using both models and the results are compared.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".