Segmentation of Source Symbols for Adaptive Arithmetic Coding
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Bibliographic record
Abstract
Adaptive arithmetic coding is a general technique for coding source symbols of a stochastic process based on an adaptive model. The adaptive model provides measures of the statistics of source symbols and is updated, along with encoding/decoding processes, when more encoded/decoded symbols are fed as samples to the adaptive model. The coding performance depends on how well the adaptive model fits the statistics of source symbols. If the number of source symbols is large and the number of samples is small, the adaptive model may not be able to provide valid measures of the statistics, which results in an inefficient coding performance of the adaptive arithmetic coder. To this end, this paper presents segmentation of source symbols to improve the performance of the adaptive arithmetic coder. Each source symbol is divided into several segments. Each segment is separately coded with an adaptive arithmetic coder. With this division, possible values of each segment are concentrated within a small range. Given the limited number of samples, this concentration leads to a better fit of the adaptive model to the statistics of source symbols and therefore to an improvement of the coding efficiency. The proposed coding algorithm is applied to lossless motion vector coding for video transmission as an application example to show its performance improvement and coding gains.
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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