RD Optimized, Adaptive, Error-Resilient Transmission of MJPEG2000-Coded Video over Multiple Time-Varying Channels
Why this work is in the frame
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Bibliographic record
Abstract
To reliably transmit video over error-prone channels, the data should be both source and channel coded. When multiple channels are available for transmission, the problem extends to that of partitioning the data across these channels. The condition of transmission channels, however, varies with time. Therefore, the error protection added to the data at one instant of time may not be optimal at the next. In this paper, we propose a method for adaptively adding error correction code in a rate-distortion (RD) optimized manner using rate-compatible punctured convolutional codes to an MJPEG2000 constant rate-coded frame of video. We perform an analysis on the rate-distortion tradeoff of each of the coding units (tiles and packets) in each frame and adapt the error correction code assigned to the unit taking into account the bandwidth and error characteristics of the channels. This method is applied to both single and multiple time-varying channel environments. We compare our method with a basic protection method in which data is either not transmitted, transmitted with no protection, or transmitted with a fixed amount of protection. Simulation results show promising performance for our proposed method.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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