Bibliographic record
Abstract
It its well-known that flexibility and error resilience are significantly improved by employing a scalable bit stream. The major drawback of multi-layered representations within a motion compensated (MC) discrete cosine transform (DCT) based framework is the increase in bit rate as compared to a single-layered representation having the same frequency, spatial and temporal resolution as in the highest layer of the multi-layered representation. This increase in bit rate is due to side information overhead, variable-length coding inefficiencies, and the differing statistics of the error signal. Consequently, much of the research in the area of scalability has focused on non MC-DCT based techniques having inherently scalable properties, e.g. sub-band techniques. However, the ubiquity of MC-DCT based technology suggests that we also address the problem within the MC-DCT framework. This is further warranted given the inclusion of syntax extensions to support scalable coding within newer MC-DCT based video coding standards. In this paper we present a rate-distortion optimized SNR and spatially scalable framework for MC-DCT based video coding.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".