Packet-Loss Modeling for Perceptually Optimized 3D Transmission
Why this work is in the frame
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Bibliographic record
Abstract
Transmissions over unreliable networks, for example, wireless, can lead to packet loss. An area that has received limited research attention is how to tailor multimedia information taking into account the way packets are lost. We provide a brief overview of our research on designing a 3D perceptual quality metric integrating two important factors, resolution of texture and resolution of mesh, which control transmission bandwidth, followed by a suggestion on alternative strategies for packet 3D transmission of both texture and mesh. These strategies are then compared with respect to preserving 3D perceptual quality under packet loss in ad hoc wireless networks. Experiments are conducted to study how buffer size, sending rate, sending intervals, and packet size can affect loss in unreliable channels. A model for estimating the optimal packet size is then proposed. We derive the optimal number of packets based on this model, and relate the theoretical derivations to actual network data.
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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 | 0.000 | 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