A High Throughput Multi Symbol CABAC Framework for Hybrid Video Codecs
Bibliographic record
Abstract
Summary form only given. This paper proposes a Multi-Symbol Context Adaptive Binary Arithmetic Coding (CABAC) Framework in Hybrid Video Coding. Advanced CABAC techniques have been employed in popular video coding technologies like H264-AVC, HEVC. The proposed framework aims at extending these technique by providing symbol level scalability in being able to code one or multi-symbols at a time without changing the existing framework. Such a coding not only can exploit higher order statistical dependencies on a syntax element level but also reduce the number of coded bins. New syntax elements and their Probability modeling are proposed as extensions to achieve Multi-Symbol coding. An example variant of this framework, that is coding only maximum of two symbols at a time for quantized coefficient Indices, was implemented on top of JM18.3-H264 CABAC. This example extension when tested with on HEVC test Sequences shows significant throughput improvement (i.e., significant reduction in number of bins to be coded) and at the same time reduces Bit-rate significantly. The Frame-work can be seamlessly extended to code Multiple Symbols greater than two.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".