Machine learning based reduced reference bitstream audiovisual quality prediction models for realtime communications
Bibliographic record
Abstract
Perceived quality prediction models for multimedia services vary greatly depending on the type of the data and on the amount of information related to the original signal used. In this research, we have developed machine learning-based reduced-reference bitstream audiovisual quality prediction models by using the parametric version of the publicly available INRS audiovisual quality dataset. As that original INRS dataset did not contain bitstream information but provided both reference and transmitted videos, we have computed its bitstream version to develop the reduced-reference bitstream models. We have compared the performance of the Decision Trees based ensemble methods, Genetic Programming and Deep Learning models on this bitstream version of the dataset and have also compared these results with the results of the no-reference parametric models on the parametric version of the dataset. Decision Trees based ensemble methods outperformed Deep Learning and Genetic Programming based models when reduced-reference bitstream data was used and outperformed all existing no-reference parametric models that were trained and tested on the parametric version of the dataset. Our studies show that Decision Trees based approaches are well suited for no-reference parametric models as well as for reduced-reference bitstream models.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".