Visual Quality and File Size Prediction of H.264 Videos and Its Application to Video Transcoding for the Multimedia Messaging Service and Video on Demand
Bibliographic record
Abstract
In this paper, we address the problem of adapting video files to meet terminal file size and resolution constraints while maximizing visual quality. First, two new quality estimation models are proposed, which predict quality as function of resolution, quantization step size, and frame rate parameters. The first model is generic and the second takes video motion into account. Then, we propose a video file size estimation model. Simulation results show a Pearson correlation coefficient (PCC) of 0.956 between the mean opinion score and our generic quality model (0.959 for the motion-conscious model). We obtain a PCC of 0.98 between actual and estimated file sizes. Using these models, we estimate the combination of parameters that yields the best video quality while meeting the target terminal's constraints. We obtain an average quality difference of 4.39% (generic model) and of 3.22% (motion-conscious model) when compared with the best theoretical transcoding possible. The proposed models can be applied to video transcoding for the Multimedia Messaging Service and for video on demand services such as YouTube and Netflix.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".