A High Throughput Multi Symbol CABAC Framework for Hybrid Video Codecs
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| 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