Index Mapping for Bit-Error Resilient Multiple Description Lattice Vector Quantizer
Bibliographic record
Abstract
In conventional multiple description coding (MDC), two descriptions of a source are generated and sent over ON/OFF channels. In this paper, we are interested in exploiting the redundancy built in MDC to additionally confer robustness against other channel errors. In particular, we consider a multiple description lattice vector quantizer (MDLVQ) whose output (a pair of side lattice points) is mapped to a pair of binary indexes and each index is sent over a binary channel. One channel is noiseless, while the other is noisy. Thus, at the decoder, one description is received error-free, while the other may carry bit errors. Then the decoder uses the error-free description as side information to improve the reconstruction. The effectiveness of the decoder in alleviating the impact of bit errors depends on the mapping -y of side lattice points to binary indexes. We propose the design of a structured bit-error resilient mapping -y. For this, the set of side lattice points is first partitioned using Voronoi regions of an appropriate coarse lattice. Next a good linear channel code is selected, each Voronoi region is assigned a coset of this channel code, and the side lattice points within each Voronoi region are mapped to binary sequences in the corresponding coset. In addition, we argue that the performance of -y is improved by assigning cosets close in Hamming distance to neighboring Voronoi regions, and propose a technique to achieve this goal. We derive a lower bound on the error correction performance of the proposed mapping -y in terms of the performance of the channel code C used in its construction. Interestingly, we prove that, as the rate of the MDLVQ grows to infinity, the mapping -y becomes as good as the code C in correcting bit errors. Simulation results show the significant superiority of the proposed index mapping versus random mappings.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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".