Fast Encoder Optimization for Multi-Resolution Scalar Quantizer Design
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Bibliographic record
Abstract
The design of optimal multi-resolution scalar quantizers using the generalized Lloyd method was proposed by Brunk and Farvardin for the case of squared error distortion. Since the algorithm details heavily rely on the quadratic expression of the error function, its extension to general error functions faces some challenges, especially at the encoder optimization step. In this work we show how these challenges can be overcome for any convex difference distortion measure, under the assumption that all quantizer cells are convex (i.e., intervals), and present an efficient algorithm for optimal encoder partition computation. The proposed algorithm is faster than the algorithm used by Brunk and Farvardin. Moreover, it can also be applied to channel-optimized and to multiple description scalar quantizer design with squared error distortion, and it outperforms in speed the previous encoder optimization algorithms proposed for these problems.
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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 | 0.001 | 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 | 0.005 |
| Open science | 0.001 | 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