Impact of Frame Loss Position on Transmitted Video Quality: Models and Improvements
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
This paper addresses the question of whether or not the specific packet/frame lost, and in particular, its relative position toward the I-frames influences the quality of transmitted compressed video. We focus on the QCIF size video frame, one of the most pervasive video formats across the Internet and cellular systems. Using the average Peak Signal to Noise Ratio (PSNR) of the received coded video to measure the amount of distortion, we demonstrate that the interval between the lost frame and the last I-frame does have a significant effect on the resulting quality. Further, after investigating the probability of different burst loss lengths in noisy environments, where the duration of data loss is almost constant, we propose a method to improve performance of video streaming over the noisy channels based on packet scheduling, without requiring an increase in the bit rate.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| 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 it