LDGM-Based Multiple Description Coding for Finite Alphabet Sources
Bibliographic record
Abstract
This work presents an LDGM-based practical successive coding scheme for the multiple description (MD) problem for finite alphabet sources. The scheme, which targets the Zhang-Berger (ZB) rate-distortion region, is shown to be asymptotically optimal with joint typicality encoding, while as a practical encoding solution a message passing algorithm is adopted. We further discuss in more detail the application of the coding scheme in three cases of the MD problem with the Hamming distortion measure: 1) no excess sum-rate for binary sources, 2) successive refinement, and 3) no excess marginal rate for the uniform binary source. In the no excess sum-rate case some progress is made in the characterization of fundamental limits by deriving the analytical expression of the distortion region for general binary sources, and of the auxiliary variables needed to achieve its boundary. The exact expression of the Zhang-Berger upper bound to the central distortion is also provided for the case of no excess marginal rate for the uniform binary source. The proposed LDGM-based coding scheme is tested in practice for all three aforementioned cases. The experimental results show very good performance, demonstrating its ability to approach the theoretical rate-distortion limits or the available upper bounds.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".