Fast index assignment for robust multiple description coding
Why this work is in the frame
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Bibliographic record
Abstract
Based on multiple description coding, the robust multiple description scalar quantizer (RMDSQ) was introduced to combat both packet losses and noisy channels over heterogeneous networks. In the RMDSQ, the description with bit errors is utilized to achieve graceful performance degradation. However, because of high computational complexity of the index assignment (IA) by using exhaustive search and the genetic algorithm, it is not practical in applications where the training time is of primary concern. In this paper, we propose a novel IA algorithm with low computational complexity to achieve a balanced RMDSQ, in which parity bits are added to realize robustness against bit errors. The algorithm proposed can be implemented "on-the-fly" by using logic circuits, and easily applied in bitplane-based image compression. Experimental results show that the proposed algorithm outperforms existing algorithms in the sense of both spreads and computational complexity.
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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 | 0.001 |
| 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