Optimal unequal channel protection of multiple-description product codes for multimedia communications over fast fading channels
Bibliographic record
Abstract
In recent literature a powerful multiple description product coding scheme for protection of progressively encoded source streams has been devised that disperses information evenly between all description packets. Also, techniques were proposed to protect these packets equally by an optimal channel coder (found by exhaustive search). The contribution of this paper is to show that equal protection of all descriptions is suboptimal when the channel varies with time despite the fact that all descriptions have equal importance. We propose a theoretical framework for computing the globally optimal channel protection assignment for a given set of available channel coders under some idealized assumptions. For more practical scenarios we propose an optimized uneven packet protection scheme that outperforms equal protection schemes in terms of the expected distortion of received sources. Simulations of an image transmission system that resembles a 3G high bitrate link is provided where our unequal protection scheme improves the average PSNR of the received images by more than 1.3dB.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".