Bit-Error Resilient Index Assignment for Multiple Description Scalar Quantizers
Bibliographic record
Abstract
This paper addresses the problem of increasing the robustness to bit errors for two description scalar quantizers. Our approach is to start with an m-diagonal index assignment and further apply a permutation to the indexes of each description to increase the minimum Hamming distance dminof the set of valid index pairs. In particular, we show how to construct linear permutation pairs achieving dmin3, and establish a lower bound in terms of the description rate R, for the highest value of m for which such permutations exist. For the case when one description is known to be correct, we propose a new performance measure, denoted by dside,min. This represents the minimum Hamming distance of the set of indexes of one description, when the index of the other description is fixed. We prove the close connection between the problem of robust permutations design under the new criterion and the anti-bandwidth problem in a certain graph derived from a hypercube. Leveraging this connection, we settle the problem of existence of permutations achieving dside,min≥ 2, respectively dmin≥ 2, and show their construction. Further, we develop a technique for constructing linear permutation pairs achieving dside,min≥ h based on linear (R, ⌈log2 m⌉) channel codes of minimum Hamming distance h + 1. In addition, tight bounds in terms of R, on the maximum achievable value of dside,minare derived for m = 2, 3, 4.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".