Improved adaptive arithmetic coding based on optimal segmentation of code symbols for lossless motion vector coding
Bibliographic record
Abstract
Adaptive arithmetic coding is a general technique for coding the symbols of a stochastic process based on an adaptive model. The adaptive model provides the code symbol statistics 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 symbol statistics. If the number of code symbols is large and the samples of code symbols are limited, the adaptive model may not be able to provide an accurate symbol statistics, which leads to the inefficient coding performance of the adaptive arithmetic coder. An example is lossless motion vector coding for video transmission when the motion searching range is very large. This paper presents an improved adaptive arithmetic coder used for lossless motion vector coding. A novel representation of motion vector differences (MVDs) is proposed, in which a MVD is divided into two segments, namely significant segment and non-significant segment. Each segment is separately coded with an adaptive arithmetic coder. With this division, the possible values of each segment are concentrated within a small range. This concentration leads to a good fit of the adaptive model to the symbol statistics and therefore to an improvement of the adaptive arithmetic coding efficiency.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".