Perceptual aliasing factors and the impact of frame rate on video quality
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
High frame rate (HFR) videos have become increasingly popular in the past few years, and frame rate is one of the major parameters for adjusting video data rate in real-world video delivery services. To achieve the best trade-off between bandwidth saving and video quality preservation by way of frame rate adaptation, it is essential to understand the impact of frame rate on video quality. In this work, we look at the problem from the viewpoint of perceptual information loss by perceptual aliasing analysis. We propose several measures, namely temporal aliasing power, temporal aliasing factor, spatiotemporal aliasing factor, and perceptual spatiotemporal aliasing factor, and use them as quality predictors for videos under frame rate changes. We also construct a database and conduct a subjective test on videos of different frame rates. Our results suggest great potentials of the proposed perceptual aliasing analysis approach.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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 it