Improved Multiple Description Framework Based on Successively Refinable Quantization and Uneven Erasure Protection
Bibliographic record
Abstract
A method to produce balanced multiple descriptions (MD) of a source is by successively refinable quantization (SRQ) in conjunction with uneven erasure protection (UEP) (Goyal, 2001; and Tian and Hemami, 2004). This work proposes an improvement to this balanced MD coding framework. In order to generate L descriptions, the set of source samples is first partitioned into L subsets of equal size, then each subset is quantized separately. Further, interleaved systematic Reed Solomon codes of codelength L and decreasing strengths are applied across the streams output by the SRQs. The improvement over the previous UEP-based MD code is evaluated using the expected distortion of the source reconstruction at the decoder as a performance measure. For a Gaussian memory-less source, the asymptotical improvement in performance, as the rate and code block length approach infin, can attain as much as 1.68 dB (for L = 3 and very low probability of description loss), with a tendency to decrease as the number of descriptions and the rate of description loss increase. In the practical setting using scalar SRQ, small rates and small L, the observed improvement generally matches the asymptotical values.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.002 | 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".