Improved Multiple Description Framework Based on Successively Refinable Quantization and Uneven Erasure Protection
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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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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 | 0.000 | 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