Towards Reduced Reference Parametric Models for Estimating Audiovisual\n Quality in Multimedia Services
Bibliographic record
Abstract
We have developed reduced reference parametric models for estimating\nperceived quality in audiovisual multimedia services. We have created 144\nunique configurations for audiovisual content including various application and\nnetwork parameters such as bitrates and distortions in terms of bandwidth,\npacket loss rate and jitter. To generate the data needed for model training and\nvalidation we have tasked 24 subjects, in a controlled environment, to rate the\noverall audiovisual quality on the absolute category rating (ACR) 5-level\nquality scale. We have developed models using Random Forest and Neural Network\nbased machine learning methods in order to estimate Mean Opinion Scores (MOS)\nvalues. We have used information retrieved from the packet headers and side\ninformation provided as network parameters for model training. Random Forest\nbased models have performed better in terms of Root Mean Square Error (RMSE)\nand Pearson correlation coefficient. The side information proved to be very\neffective in developing the model. We have found that, while the model\nperformance might be improved by replacing the side information with more\naccurate bit stream level measurements, they are performing well in estimating\nperceived quality in audiovisual multimedia services.\n
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".