End-to-End Performance of Robust Multiple Description Scalar Quantizer
Bibliographic record
Abstract
Transmissions over hybrid wireline-wireless networks suffer both packet losses and bit errors. To facilitate delay-sensitive audio/video communications over hybrid networks, low-delay error recovery techniques, such as forward error correction (FEC) and multiple description coding (MDC), are utilized to provide protection against bit errors and packet losses, respectively. As a means of joint source-channel coding, the robust multiple description scalar quantizer (RMDSQ) was introduced to combat both packet losses and bit errors. In this paper, a novel RMDSQ system is proposed by utilizing both MDC and FEC-based techniques. In the sense of rate distortion, end-to-end performance of the RMDSQ system against packet losses and bit errors is compared with that of individual FEC and MDC-based techniques. Instead of the traditional two-state Gilbert channel model, a three-state Markov chain is proposed to model hybrid networks and work as the testbed. Experimental results show that the proposed RMDSQ system achieves higher robustness against increasing packet losses and bit errors. In contrast, FEC-based techniques achieve better performance against bit errors; however, their performance deteriorates significantly due to packet losses.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".